{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection with Filtering Method | Constant, Quasi Constant and Duplicate Feature Removal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watch it here: \n",
    "\n",
    "Part1: https://youtu.be/nPHU1CpX4jg\n",
    "\n",
    "Part2: https://youtu.be/m0fs0v5GGlg\n",
    "\n",
    "\n",
    "Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unnecessary and redundant features not only slow down the training time of an algorithm, but they also affect the performance of the algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several advantages of performing feature selection before training machine learning models:\n",
    "- Models with less number of features have higher explainability\n",
    "- It is easier to implement machine learning models with reduced features\n",
    "- Fewer features lead to enhanced generalization which in turn reduces overfitting\n",
    "- Feature selection removes data redundancy\n",
    "- Training time of models with fewer features is significantly lower\n",
    "- Models with fewer features are less prone to errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is filter method? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features selected using filter methods can be used as an input to any machine learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Univariate -> Fisher Score, Mutual Information Gain, Variance etc\n",
    "- Multi-variate -> Pearson Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The univariate filter methods are the type of methods where individual features are ranked according to specific criteria. The top N features are then selected. Different types of ranking criteria are used for univariate filter methods, for example fisher score, mutual information, and variance of the feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multivariate filter methods are capable of removing redundant features from the data since they take the mutual relationship between the features into account."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Filtering Methods in this lesson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Constant Removal\n",
    "- Quasi Constant Removal\n",
    "- Duplicate Feature Removal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download Data Files\n",
    "https://github.com/laxmimerit/Data-Files-for-Feature-Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constant Feature Removal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "   ID  var3  var15  imp_ent_var16_ult1  imp_op_var39_comer_ult1  \\\n",
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       "\n",
       "   imp_op_var39_comer_ult3  imp_op_var40_comer_ult1  imp_op_var40_comer_ult3  \\\n",
       "0                      0.0                      0.0                      0.0   \n",
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       "   imp_op_var40_efect_ult1  imp_op_var40_efect_ult3  ...  \\\n",
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       "   saldo_medio_var33_hace2  saldo_medio_var33_hace3  saldo_medio_var33_ult1  \\\n",
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       "\n",
       "   saldo_medio_var33_ult3  saldo_medio_var44_hace2  saldo_medio_var44_hace3  \\\n",
       "0                     0.0                      0.0                      0.0   \n",
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       "\n",
       "   saldo_medio_var44_ult1  saldo_medio_var44_ult3          var38  TARGET  \n",
       "0                     0.0                     0.0   39205.170000       0  \n",
       "1                     0.0                     0.0   49278.030000       0  \n",
       "2                     0.0                     0.0   67333.770000       0  \n",
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       "\n",
       "[5 rows x 371 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('santander.csv', nrows = 20000)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 370), (20000,))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop('TARGET', axis = 1)\n",
    "y = data['TARGET']\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constant Features Removal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constant_filter = VarianceThreshold(threshold=0)\n",
    "constant_filter.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "291"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constant_filter.get_support().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " True,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constant_list = [not temp for temp in constant_filter.get_support()]\n",
    "constant_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ind_var2_0', 'ind_var2', 'ind_var13_medio_0', 'ind_var13_medio',\n",
       "       'ind_var18_0', 'ind_var18', 'ind_var27_0', 'ind_var28_0', 'ind_var28',\n",
       "       'ind_var27', 'ind_var34_0', 'ind_var34', 'ind_var41', 'ind_var46_0',\n",
       "       'ind_var46', 'num_var13_medio_0', 'num_var13_medio', 'num_var18_0',\n",
       "       'num_var18', 'num_var27_0', 'num_var28_0', 'num_var28', 'num_var27',\n",
       "       'num_var34_0', 'num_var34', 'num_var41', 'num_var46_0', 'num_var46',\n",
       "       'saldo_var13_medio', 'saldo_var18', 'saldo_var28', 'saldo_var27',\n",
       "       'saldo_var34', 'saldo_var41', 'saldo_var46',\n",
       "       'delta_imp_amort_var18_1y3', 'delta_imp_amort_var34_1y3',\n",
       "       'delta_imp_reemb_var33_1y3', 'delta_imp_trasp_var17_out_1y3',\n",
       "       'delta_imp_trasp_var33_out_1y3', 'delta_num_reemb_var33_1y3',\n",
       "       'delta_num_trasp_var17_out_1y3', 'delta_num_trasp_var33_out_1y3',\n",
       "       'imp_amort_var18_hace3', 'imp_amort_var18_ult1',\n",
       "       'imp_amort_var34_hace3', 'imp_amort_var34_ult1', 'imp_var7_emit_ult1',\n",
       "       'imp_reemb_var13_hace3', 'imp_reemb_var17_hace3',\n",
       "       'imp_reemb_var33_hace3', 'imp_reemb_var33_ult1',\n",
       "       'imp_trasp_var17_in_hace3', 'imp_trasp_var17_out_hace3',\n",
       "       'imp_trasp_var17_out_ult1', 'imp_trasp_var33_in_hace3',\n",
       "       'imp_trasp_var33_out_hace3', 'imp_trasp_var33_out_ult1',\n",
       "       'ind_var7_emit_ult1', 'num_var2_0_ult1', 'num_var2_ult1',\n",
       "       'num_var7_emit_ult1', 'num_meses_var13_medio_ult3',\n",
       "       'num_reemb_var13_hace3', 'num_reemb_var17_hace3',\n",
       "       'num_reemb_var33_hace3', 'num_reemb_var33_ult1',\n",
       "       'num_trasp_var17_in_hace3', 'num_trasp_var17_out_hace3',\n",
       "       'num_trasp_var17_out_ult1', 'num_trasp_var33_in_hace3',\n",
       "       'num_trasp_var33_out_hace3', 'num_trasp_var33_out_ult1',\n",
       "       'saldo_var2_ult1', 'saldo_medio_var13_medio_hace2',\n",
       "       'saldo_medio_var13_medio_hace3', 'saldo_medio_var13_medio_ult1',\n",
       "       'saldo_medio_var13_medio_ult3', 'saldo_medio_var29_hace3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[constant_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_filter = constant_filter.transform(X_train)\n",
    "X_test_filter = constant_filter.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16000, 291), (4000, 291), (16000, 370))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_filter.shape, X_test_filter.shape, X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quasi constant feature removal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "quasi_constant_filter = VarianceThreshold(threshold=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=0.01)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quasi_constant_filter.fit(X_train_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quasi_constant_filter.get_support().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "291-245"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_quasi_filter = quasi_constant_filter.transform(X_train_filter)\n",
    "X_test_quasi_filter = quasi_constant_filter.transform(X_test_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16000, 245), (4000, 245))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_quasi_filter.shape, X_test_quasi_filter.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "370-245"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Duplicate Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_T = X_train_quasi_filter.T\n",
    "X_test_T = X_test_quasi_filter.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train_T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_T = pd.DataFrame(X_train_T)\n",
    "X_test_T = pd.DataFrame(X_test_T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((245, 16000), (245, 4000))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_T.shape, X_test_T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "5      False\n",
       "6      False\n",
       "7      False\n",
       "8      False\n",
       "9      False\n",
       "10     False\n",
       "11     False\n",
       "12     False\n",
       "13     False\n",
       "14     False\n",
       "15     False\n",
       "16     False\n",
       "17     False\n",
       "18     False\n",
       "19     False\n",
       "20     False\n",
       "21     False\n",
       "22     False\n",
       "23     False\n",
       "24     False\n",
       "25     False\n",
       "26     False\n",
       "27     False\n",
       "28     False\n",
       "29     False\n",
       "       ...  \n",
       "215    False\n",
       "216    False\n",
       "217    False\n",
       "218    False\n",
       "219    False\n",
       "220    False\n",
       "221    False\n",
       "222    False\n",
       "223    False\n",
       "224    False\n",
       "225    False\n",
       "226    False\n",
       "227    False\n",
       "228    False\n",
       "229    False\n",
       "230    False\n",
       "231    False\n",
       "232    False\n",
       "233    False\n",
       "234    False\n",
       "235    False\n",
       "236    False\n",
       "237    False\n",
       "238    False\n",
       "239    False\n",
       "240    False\n",
       "241    False\n",
       "242    False\n",
       "243    False\n",
       "244    False\n",
       "Length: 245, dtype: bool"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated_features = X_train_T.duplicated()\n",
    "duplicated_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_keep = [not index for index in duplicated_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_unique = X_train_T[features_to_keep].T\n",
    "X_test_unique = X_test_T[features_to_keep].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16000, 227), (16000, 370))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_unique.shape, X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "370-227"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build ML model and compare the performance of the selected feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_randomForest(X_train, X_test, y_train, y_test):\n",
    "    clf = RandomForestClassifier(n_estimators=100, random_state=0, n_jobs=-1)\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print('Accuracy on test set: ')\n",
    "    print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.95875\n",
      "Wall time: 2.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train_unique, X_test_unique, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.9585\n",
      "Wall time: 2.87 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.556291390728475"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1.51-1.26)*100/1.51"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection with Filtering Method- Correlated Feature Removal"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dataset can also contain correlated features. Two or more than two features are correlated if they are close to each other in the linear space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation between the output observations and the input features is very important and such features should be retained"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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6PPT6TdxxD/UtamiM1WQ5XocRo42+3Yjx+BDEGOKM52l89J9fjSSdxKc/thMXvOkydJo1zLoO1h+bYd3hcxiig1t/AqR94L6N1IWIMN4+Ar//hhfhrW/5E3hM4s/++6X4ytc3wKgMA9fFub/yZSg9wPrDD8Yll/0OYqUR1XaKlkqsW4BP0W5QfkLLPGDQB3oDDa1WopFwI7GFDfen2LKTOt41mLqFxzSUG8BaK+KVc1++Em99//Ho52N491t24hP/cCMM+qIy94QTj0FnxeNQqCfgJ/c67MyBO+6lTd+TsP4JR2PVgQeCCoMqbmDoLG64aYBtU7OiSXLLj6YRRRkK5ZBTfa6moH0H3T5w5JEGV3z1qVg1eSSuv24tzjl9G1TewJnPaePP//wURPoQXHjh9bjscz/GRPNA6DzH7533EQyKAoesOxaXfO5N2DrTQw857tm0HRs2bEectGBoAGNT1GtBVLQguw/NKpy1yE5K609p7OV/9mkKLAP1Pt08e75w1BEmLnNZDK0F/Ay1GVyO3CqcdPJGrF3TwLVXPQdveM0AY6soLdshm3mJrqFmDoHxGYpBH65IcPZzJvHBj8+im30RVlMXOkbNJYBN8cnLTsOznn4QUszh+afNI8vPxPEv+D38/acjGAxw8PoenPoqdk7di2G6Gj5dAa/XYvuWE5C51Rg/+AasPpAy7FlAU+SRYudUE1u2tLFjWmFqWmNqa1dEHx3qa/cogfBwFugPEyi9CkNXoJvtgMOMqAq26hSh9GF8F6/54wRv/+DjMd2bRdZLoXA1rEuRD7bhqutjHHnqP+GWm5+ClzynD9X8Dl78iivxZ/9zPRUCAWxBq071Nw8kt6PTHoPN5tHqHIys6GLQawgtDOYQJ9vRptl5fi0OO7zAl354PIb4LrZPbQR0jChu4oQzboRObsVM92XYOtdBinvg41nc9PXTUDNNHLDqNhw0mWOQ1hDrGuI6kLRobWlgbQuJWlGqOIY91j3fk5Zz2JMUWAbqPUnd/SBtMtSymUduqyiE+XI+l205qpetWXMgLr7yxfDaYG6ugamdY9BRExHByTZhnEPuEsSNBB0N3PjVFK9/1VV49Wtfgv/3fc/D0BYY8+OomS469Tl85oomDl09ic29DlYeeiSu+vy/oTPmYWHwkvPW4DWvXQOta7j5phw62YZ7tkaY6nmsXfNEXH71a7Btuosc85jPejBthz9/93dxzNEfx1GH/QPWTv4lTjvtjbh/x1b0M4daex4q6onPi7iWgNoT3PlrNBNYn8MXCs5q6KyJ8Vobn/mnO/CXb/4s3vSWk/CHbzgcoDl8sgmJb+LApsPnPvcarGw3MD0T4WmnrsTFFz0frQmP3vwc6hhH2uMEBli1BfUkRQ0JunNHQNcSNNtaNAa1TRAVXtj+TtzFzLzDOc/4CI477ABc9YOT0VX3YRDnOOusU/GWP30B9V3QG7I9mti5o41hv4WovQWfvOoYIBtDVCP9HfLhDIZ5V3SsvW0g1h3JD4aTWhfQWdCGWeiT1N1bHv4L5NjHT5Zbah9voD1dPE+rO+IX/yLCrwuW23Dopyl0FKE9FmH1QVtw2SVb8Wev3AKYdejiG6g1dwBqG+IGjVHa+MJnuzjzly/Ha964HRf+r0PgaVZudgDxTviiJqptW++zsH4WqQOyQQ2337NVRBx5P0Yz7uOI9T/GivbxeMcF47jhexP49Rf8NYpsHfKMamYWE2MrgP7pSPRJSIsGeoPTkA7OB4b/HU31F5idOQUr1q2Bb2pM2R3ILI1KaMxCfyLUxLBEUti8jiIn97kGjck5/NM/fwd//Ls34o3//Ry87b0HQ2MCqB8MuFOAoi2GJrfffDt6g03QPkM2swrbN29Cz1+BpHUPYmgo2wa9pbSak5id9nDdFWglJ8ClNbish5ijjfn2xxHFv4T77j4cpx/1Q4zr0/C9256MifEUhT8Emab2C3D0MT9Be8VH8NbXfBPf/1YNLzsjwtz04zFlr0DtgJR+rKBdAsMlUdxHrd6H1g55qlAUwS+Kx7C05KRK47Jl4p4eT3sq/WWg3lOU3U/SVZoAWAI1ZdSw8J7e2oKq3EABRx9Vx4UX0cXRzUjU46DNIYj0EUiH0+irBN7VcdVXNuLcc7+B33/D6Xj/+8+HCG39BLZtPxBfvirCxjmLW+9x+OVnXI+N2zM88/QM09u+h3OefxluulGh8DmGczVc8IaX4QUvPREUPNx0cwIzWI8c1+I5zyfXOIvEdMWgIx/SbHuA008GzjoDePpTLU576hzOOGMlVOFQDIExcwhq8ThsnoDaIFpSLeCyBM1kJeKoCZtY/PPF0zj/tR/F+z96Fv78PYcCdOLkI9xxewdfv/pubO338N0fRDj3nP9EvTWDE09SuOXmefzGC3+ETRsOAfQ4BmkOpdpwyRx2zM2gszIWlb68mAr633aV+DpBPIN4DLjtduCscy7C0ceO4au3PAVQ9P+RQJkM11x9D358Q4aXveJ4vPr808Xa8uqvt9FHhNQP8IJzTkZDWfTz6WA2zkk21xgOcpmUrKOdZT9w0jR5p38U8f2xRMlrWUa9n4xScUe235R1uaB7hAIc5eVRgTZdfmqNwlnkWiPNNdavBx534l24+YbjUSQOsTsQcXYIagAuvrSH83+jj1qzhv/23EPwNx+cx6qJWXTGbsUnPnY8vnLVOP71P8fx4Qs/jw1TpyFGCx//tz6OXXkt7r/lFbjgVRtw1XfXw7hg9j2dXg/Eh+B1v7cOMX4VzdbH8IFPPhF1tQ02r6EYNtBeYTDMgD/8gwH+8LUt+MhDqXEAzwBwH1x2ILSvoauAVkS7kTq076JOwxF6zyMPHAMf+4fb8bbXHI3jjvso1hx7Ky75lzuQ9E9BbWwKH36PxTU3vh8/uvud+LXnR7D918Aeeh4++53/jSMaP8a3rluHv3vXCrzvE4fA1ProFvcDeht8sR7z/UKsJVV8J0z2yzCmC6u2QZsEN94JvOTsDbh7w7vwhvc7fOHiTWi0ZmDrPXz76nX4wIXfwwvPOgmfvvIYzNpbMcRqvOctHeiYoqk6PvnPZwH4CWIzACIa6z8REZ6AuJiHocVkcxYqmkV32ENTHxm8DnJ644wsszLbm9w1LTiXebWq++/Lv0um2H25qMtl2yMUEG90GWA4aGMYG2H2vv9EbDcgL46Dd0+DiVMcfuh6/NOl6/HHv3cFvvuNSWi1E32ciYsvvxav/p3PY4jHw6YW5zzvJtSjcaTubjj3nzDqbCQ4FRf9rxy3/vh7iLEJ573imTDuGJz7soNw6SVfw513TuOyT8/iJS95MoY58MJfPgo3fM1h05YYKtV4xfm/hF6/QNzScIVCs2MwHM4hrlPATmu8AsgieKXgEzp8SkBF8GC/SGdFY9g+fRcc1qGws4g8XaRGuPDCz+BP3/xNGPwWbr55Ci8581OAbQHuNgFyi/uFm/37v7kO89ubSLAGb/iT30U2m+BFZx+Fy6/4Ei67Zi1+86qzcPqZNbRpkDI8ChHuQF95uPZ25Okt4oFvoHagcOvw/a9txK/91scwN3UqItfDH/7prSjMd+DtDQAOQ4xfQs51zeQPMK+OxAuedSKu+ZLDbbdOoY278eqXnoKiS/n7kfB6Hmk/Epda1HgZ5PdiUDwJ1lCRHGjXU9g+LTy9bDbu2n8I0MsgvStN9t2rZaDed9tmr5RM/CEpLo8Xua3/8Z7n4847+vAuRieaxiClE37g2CNr+Ju/fzr+7ZLr0B9YPOOMIb76lftwwRuPRyM5TFTdiqJAkjRh3Qp0xo/F7M51iGs5tm6ax+tedwqmtgJve+9xVALBxy/6NRx99BegsjaGs3fDdp+MejvFS19+EL582SZc/m8z4kv6Le8+GZ1WD2l/JdqNGC7PkdTouIlLegsYD2XpxInVIKcY2EbtyXCOYZAO8PLffTq+dvUW3HjdNKylRYrCfHcn3vvO58APVkCZGnTzRXBWoRjS97WB0YfB14/B9m1b8Wdve5K4U/2T/+c5iOIB/vYTT8BxH74bW3eOodvfCdhjkNQsGonFK1/7FBy4uoEUDm945/H4wDvpX/pw8dLanUlx3rnPFY+EHZUhSlYgip+HLD0LRVYXnypezeK4k+uoY4jn/urx+MrXPG6/43Z0sxvwtg+fglpSIOvVMNZqYqwFtGKP9cc3cfqzDkNE53o0v6cjqYL14Eqj7ErV717pWcuZPKIU2Mf1vJeLt4cp4MS0w3qbO2/zws/O0TBjKMYgubc+Le71Yh5SeD83S7OOrhiTFNb7NO/7nNYifugLP+et876gcYXPvaRL64qCxiddsbTIaBUjz6Z9kVmfpb2QNtOw4d106P3/97H7/VGrf+ybuNG/9W07/Jzzfme/5wvb83m2SYw4smEuxirez3vv58S4xrNQfqN3fot3rpDypNb7frpJjHX+6I/u8DGu9Nf+UIrlLc0/aOfDcmUhf7EIKXre50W456y8E+rpfX847wvf9/0s9YUjNWhBkvpPffIyf/i6u7xG4X+yyfvUb/Pz9n5/5zRdT836c88rfM9OhbS8pzlOqLNYoIzk7TJvi2GwTLHeX/aFKb/+iHu8wdC/812b/dbpe/3Qpn6Qe3/HbdP+nHO+62Ps9BdckPquz/3M/FAMfYb+Zu/9fd4Ocl/wZTZOafAiJ0Jw0mv52B8osLz2eUSnvf0vMSuhuOh6VMlfLM75HWp0kpQDiV+HYpBBOYtOs46sF0mILi6uEzQoIhWmtuhnEDfTlKBYRivpozeVYTiYhyooopih1ybATgFpAyayiNFEURhxXpTB4+LPXYXfeOlH8b53zmLbtgPRx9fx6j9ZiTzP0EyaMCJnPkBce8a1rDSJpoCDXpJocsLz4JFZDHjYHGLA04FGDYPZexFjFhf80Ydx910DeJfBKwuf9MCAMzSWyYoeUpchtQUyXwB9jXzGCzeMLEMc5Sgyh1jRtwe59+3iKOmbV2/Axk1bUTPT6Pe30C8qE8PM5kw2/r77H7fi05+6RlTk0nky5zTU6SP3odTzWYbtc9Po5hZW1/AfX70bL37xu3DB+V/HxrsjJOjjxb+5GqvHDoV2EZTz2LFtE6649Huo4T4U6R2IwbBdRZC/S4gtL1og9GVNB1KUS1MRcln7Y/8bp8tAvf+12SNa4gh12CJYsDF+YKPRQdbPoWwMlWvYYSRO6xHvAKJBMIzxKbZP/wT9fCuxS3xwaDOOPgMEMkyXOPgHkoZBvemgomkUAxo6Ozg9DxvfJfJw+vF3NkcUdWF0httu2YbLP/dVbNj8HQxwKT74v5+Ben0rbSBlQhgO6Ao1Ri5aZgpJRPkrFQoJQuWmKHWDxe2pyEEQxwMo6hAjx5+9+Rl40ik78a1rP4OZ2e3Qeh6Z7cNiIA77c2ru0RufpuvXBCoaAKaPuE7nUB7T3XsQmbq4MJ0fziCzBZrcTrU1iSVWmIvwng92cNABBwL5SrTrq3HMujre84Gt2DT9FnztyzehKIaodX6ALL4JhalhtjslwppaI0K9RVHITvSK7bjplpvwhc9/G9M7tkFFX8IFbx/gmGMyFO5+RBEDBCi04jZyfBtPfPKP8CevP04AeHKyhRrGgGwdvJ8QJXkxZnpEe81yYnubAsuhuPY2xfe5/FJYGn2YWPSpGbvQk1MsPOhTmpbljBXosB153kItYiDVeeR+B7xvIMFapEOLKDHoDefRblOe3UesxpANWuJb2TFM1vBg1OoJUrcJmd2GTvJEFHYIThRwO4GogS33ekzNKMzMJBj6eZz23+qIoFEU86iplaA7VKWouEd3nw7ebAPUBDSDvfpGMOCQoLkRnB0T/9ZpMYukHsMVHnFUw523ANv70zjmqNWYbDOqDBWsZ+Ept7YtCRbgPAMaJBL+i17orG0DZg5e92DzlYjjBgqqMvoCEXrioW+6N40Nm7di/TEHoaUaqMVAkQ1hu21gbCd+cN1dOOKgk3DA2s3o29uh9a0JIDkAACAASURBVDGI9aGy0WhtBuvngWgbtKJP8BWY3roa99yqoOMG5ooZPP4EgzUdRnzZjjpDw2AS+Rzw4w1bMFar47DDJ2DjoQA4BoyzWIeL+tA2AnXlVcwABIGnDpEx97mOuFygn0KBZaD+KcT5xXg0C6AOBp7iUdgCsdFwmYXWMXJGwI5p3TeAt3UUwzEUegb1Rgb4NpRvIR0yFiARnSnksI6BZWshenlELpiWcTG8RBB3shzvD/poNMZCiMQih5JoKBa066a1IJVRhr4PPWii3tyK1M0j9odJqC0tnH0dNqc/T3L8QyjPCC8sQ0/0hj2DvnonGitDtx11nQBFH4gOkaiCVGNTwy5MzEgxvaApwkyrDbdyc5XeBOnfWYnOsQN1lGk5KTX1W5D4A+WbQdZFo9aGy7vQqg2Cr+Emrc3h4i50tEI8EyrFVQl9Z8cYugFqWUPu02xfFMQd9djpptWAVjpODWBVQwQZqhhIFPZBbRsS1YTKHWxcQ0zH1zBIFYMNc2+VoiaH3GeIqcXC1QbdsUpoBYbqqiop1Vj+Zz+gwDJQ7weNtCeLSDFscDRPOTVzSmFE1hsBlC1nBK8CqDFWIcN1t4GE0bHvh6FDJDshPkKKwkKrRMBSAorQwYaE16LqnwPyjphsizadcI0UuQBp1JdAXbTeS6IyfFQtxUCiiTfRtBaWgFbbIpaO8Oug/bhw/4x+gjiGVhQFVEFvB8HAo4jgFDUeFAqzGdobmIJs7goMGhSWR2g4A6epvkftEXKbQcZNeXa4JvfO6I8hQjuDF/AghyraFDr8WtuAMRY+G0JRaJ9REb0BzzBnFMNEFl4XEhorERoCqDk4KXdN6GAYpIaaGqS/psn4QCY9yRsHQDkShxNKC3ktRYEuGiKCr8P0eaZp0S+TU0z5eN6DrY3DSAQchuQqgbqM+B4qsqAgI5fL/+y7FNjLQM2OPSIWD/sbsCZ0QIV58SwmgU59DXkOkBGSCNeORroSlFqikZCkXnXhqPvLwKYy0AZi/UXukPepesZxIybSoqY0ykmIoHPX8jxIO5HD4VF5las2qkT2R6RjyUQ2Kt6Nwl6NhEFycOQUf+rB7x/Noypf9UuwoHe5+RD/z60JcumFIKl0tMQ/lttBu7XBh4QalKpxZA0ZaDbUSTO8FqOEg3Ji8nJ8Tmf2DELLEFs9KDQELJmuw5wAFNNQaCGiQydu+Km+TCIBGhl2tgZuRwZja+ZVZijYs9jODjvKvMhvGwF0/pKvFI6bUVMwgAN3+FgqCltCecgq57gPCk1oUKWPE1Ggk3C8AuEsF/sYAx2EIAjKsB7bYTEtPlFUcRicb4kpPX0QeozBS3qDsPJg0OBClf2UZMqgJKBDJs6qwHf9GCC0YAXJ5nPMpID0ffZ/ctG8Nyu0VmBQgRZs0Q7SHZljqj7PEod60Ibx0T0qDeHRsrFE1XXVruwvPGcfWmxfOE1fYnIw2IPRQSzGCU6SqJJ/dCv5X859L1djEQyk5PTro8jBERg4LDpCXO8zcHUYM5hooaEiA6XpgtPDGPoxcDBc8qIJQxAt21LklJKOrBrFwxqNOIIe6UjjlmSjnLNy8/nzUtKyoOxWivJKHgymShGvEzNsMpM/9Si//6nv7NGHVQEZb7BsH3GnR6Jydy0so0OsLtKa11Fwi8rI4pYsGiOoUJuAtef2HuMYBu4zqIIUIuuWQUYOk4Yp8rYOgW29OAkNLafaAkAMseUxG3SlHUG7BqgatGwMkqvVMLQwJGhJuSvRC5Nh6qFeRpPbZPBEtj/LHjhLKRAF8BQasA3kPYaPYStyogmWfEYxui/pwrpXfY1AWa4YJCp5HOoh79BdLMsVB696jJ5rjfgaEecchmkwfQbkNaK9odgPqXbCYrNIQhxGjuEqgbEfZdYv75fvUDRCDl8i25R1Yj3KVY3c9zE05dMyAMr6s8JCn9ACUnehwqP0TyhG2TajZViCFaOPRs/FOy/91TBcGVc+GTQ3Wtj+Us+wWhr9ZH8838tA/UAScQZkx+VYVTHViBiSidZhHFR020h5aRoMEgw5oRQm4sBgH9awkQXlfl4EfOR4wsHZV9G3g6uAOvSI0R3w0fMHlmzXO4udvZJX8vlCLxO/ysQs2ZuiTkNVkOp31+T2mStSqTqq2gSQJBdrEYn+HduEb3HwsA08fKnWx+W7V/RzEYBa+WAJF97nUpxLeOIPQTkW60HBs/Ku0uQkAxiR2yZHy4nA6TlwUw/oQvlJeDceykC5r8rgFNXwWKQKPCXBxX/IhYvvOW4ysryhnhU3xmsObpHQaAUtk48VIx8vMt+AmSYZkwmf/k/CHa7U2NADQA3h85JLZz14jxOVY/Bb1sHQRROU5goAUJp7ASxH0FbxPgkBeXlLLEMZeYVvUu5O8KWWDGlS+eqoqsd2oEUmVyc8r0CNrlSpA8P2qMvklqkhtCZTI/4OywQCLXjBeePRPHbJfpeLxVJVfWnhTll8aQauynSY6MUToqGGTmASlNB04av9+mSPAzU79e4BMXA8BTddfF2YByruOm9lecy2kIYoKDslVxRgMSuGiCMF5elfmF06NApNgsPBwcLhwI7cXOzDVOMS2SLBNLR09Vt++KA/u3+v5NgcN7/60HE5mOQ2B7WGyxVULbz3oIkLt/agT/f4g7ABxzKS8wqcmywxRbuN1xRZhLaoJiYBqqpkKhMBATk/0okgETjQ8gVuJJJ7FFBJgqxVWohgx28pB2bbUS+6HKkEfhpG8zvhJukDhDESqcMdoAiMmM732Za0UBTHQ1WeLDNFEhngmosiBRUU+eQt2RxkcFoG9mXeLD/FaeSUKy6db5IadBFKTsKKzJnaMMRGCW8VcSOzLhOGojtRHpZy8zq0bkggWs+VByc46XasE+vJC67oWAcCTWiD0GHZp8MfaxvozjqxtRZFALyWSY70I50UVy5M24SxAzI6PdEhlxVJKYsPhWS67KePLsf5ABAOhQttwPOl40PaLbwUNnjZd0K/8VwNaU5S5cQlNKkS3L9/K3Tbg7VghwhguphJ6JTshMYkYYVCbQEyVlEC6tdyeDSiFCbZicKOIxJRB1DT43CO6D0jDmpUXgeo21pyTCLU5jKSWeghvKZZbsj55xNzjAxa6Q9c6jM9XjjARtAmbOsMsxxxLQ5cPxe+tSA7X6z3bs5GOt5unu75WwKEHOTloBDsowyZnJwHOd7FIwyAcKfi4ti27EYEG6ZDEUU1+ANAcUYVmS7FDmFMQQuoM6UQ1FZkXQRj2UTjdzVEdLIkQExAIXjz48DBEky5uiJ4BwAjcjJ/LoMJWJS9crVFlb6qDuF5VR/e9eTaySBI+nyX2hYUgYRvnC/l65w8hFYlB+/oW6QOTcdIjnXnwU7HMrIs5HbpE0WFjTypN2nF+JTlaoDjwgctDwFcWZXwW9af/9GMnbSs0mUeZEJYNs4qrE/1y1ukBftiqaoim6BkfFi+sp04TgTMuXrlN0z7UTxKOj+gHEIvGcShcMK18bpqy3Cbay5TMmmsvlXzMGpSJEZ8VSRdj2L1Hqms9wJQjxSVxC/pHDgFutXcBk1PZ9TjjaZw670pPv6hGpoNjTOf04LWLVxx6S149R8cjsPWzyGya6RzOt3D/ffV8JEPeHjThaO4RDg3h8hRJpjCR9fhrf/jRDQaDURR4GSqQfvgnP5IeXkq3M/iPfmOy2AZVA5p1INRLVx/vcbFn7pT3H86p2EpK3MJ7ENuJi6m/WiciQRD2mShYSQSyQK3ukv9RwaJDCSOeWqFiDAkAJSj7FUIJ+IRJbJfXvMmQSNoZ4QNS3LxnFS5LKdYJIdnVBjKG6n65xKBPeGQuOHM1Zbnph7z8PK+BificgKo+pfiZMkVFf2CLALRAscuxQkV0MaIa1ARSXiG76IYpCo/27lcDchqgyuCkJesEIiRZiBcPmXajI4u/cOFNOijGyYrtURGOWmuMjkjUo2R9JMpMaxGJGvGjGE+CopAJuVhPaq6yI3yevR+uQoVdAoTqWeUd4o9KA8n+yPAyMkiTAAMCvxoHtUqVyr5gElDiSqnkKTqh6JLzwmVd2mQtRNJnCKp9TDW1vjjNx8hm9QRxR4yIT0MZunRJMDDzHsvaH2Qsynng3IgUd4XlpME6g0w2eHQRQ1583Z8+ZoxvOjpKxEhwvHHDbFlk8PszE347OdPwnNfMAfkq0Wwlqt5fPdbGs94OhuCy1IOYHJeXOqNQ/sBCn0ltm5+JjqdDpKEnZUNX3ZgDnpp7YfoqCKbDCuCUiwLRZkYuTBXIFWbYNQx+Nd/GeD83/omgKeWWgzcIg184MNsi0fptWrQ7yZ7DpxqpbKbx7ylRwY6QarE74W3hXMuB5k0fxhhpZyXQ21p/hUYle0kc2UANU7EbLPqC9EmEY6a2VV3F7L+GU8q7YeS6x0t5y4pLa0hOX1yz+R1+YyTUejvvF5Yhu+SxmJpXQVAZQq7vrZY113v73r1QKoz/UCPwBDxfJQ+0hJlIkvrs2vae/5qtFy7y63qD0vfC9dtZBiCqpt34NA1GW7Z/DzR9KI1a5D/hLbZXcr70729y1EvoQwB29r1iOnyS7rSBArVhDc5ks4cfnDzd3DO+eP4nZc9Ayee2IPYDhe0WVai0uubdA15X7ms46bPdsBsgraHQtO6zn0FUfRsRFEEU7p+XFKEn+kyjCkufdm5CR4eDbVKBqdWG5Dh61BqI7yfBSJqGrTEEOOnZ1J1xJ/+1p56Kmp2IiZglYIhhqyJKT8WMcJDdBHR7qAMOch/A0AQZKk7TBnxSpEVU0whNPMUq/A5JwGCmvB5wh2Rqw4gR+ihOCKVvgBHLpzloJe/8ltuNHtO9GESXeQ2CUnVPXLDzHf04D22X5gIwgYmlckpHlBwkj7pQB47ANpiehTNhJBWvEfDFesI1IF7D4wCTdYpTye7XWnHcCXAMrBcVP9jmzMt/vJbHjRdl5fkKnC+1TwZyhre47/8ruw3fFSBvUyqQV7tJS3a6FM8EvpqSLisu9CTE18ltllMfW+eVZvSC/VZyLykRalNFNqgXGlQvbNcGXSFaaKu/izWHn2UWJbyS09NoUdfBL9Qm//qyV7gqKsiciBzYPLgJgjDIHnRe1bGIM8cEhPhy9fM4HnP3i4O0H/7lX38zYXrUWt2EZsmfE6ZaYLcDmDNFgwHBa7/YSy760VsoGk2qxxsOoBxOZpxDaeceIgsk4jTpb1C6OSVbGwXGWxV1gf7JbiwG5SDhKpB1okz++kpizvvuh+Zp1xdPO8jZdw+WZqPcAMCBCF94Q5FzhjUixZzDemTQy3X4YuPls+WKfAYogBXVNLPOX2KZmM1AQWgpuSwEifJZE3RmgsiIQ6PgmqakRPtolrD49jHH4GxdhimXBSVC6P9nmIPwS7tifoRtMgGkJdSyIcaSWseScKZfRLK1OFsC05vx4pVDu3ODvSKHSiGa5CoCYl355VG3bShGx7PPO1AYRq4J56x5Sj3swaJgli6pTSaKfdcdqnNw25BdpzA0UiHWdihZ2rkqhStmLH6QIOJlYeB2oacyPkVi9OsuFXJvOSM+KA6KuCmmOFhl6n6ePl3mQL7OQWq/s9qLMioS7CWdUYYMyOsjpgZ8XWujMK2K9+3yPMeajGNfogDAWFo9PNYOPYqUC8uIUviK4jzmmExhyRS8HTmY2gFthbOzaFWj1FgTpbgkYnFdwIVPoxqIMsL/PjG+3DG6Zei2TwAus6V5hQ0xSbiByGFzadw0+1/ikTz2wpsKZuulsYPpwkJu8G7XFgOUJbJSYWko2oXVwMpTJzAJOw4dOLDGB0IVlJizFFyBbsB4gX5oqzXiNWjXTJ893BKufzOMgX2RwqIfF9EbEGbJqwyAyPDc1HJLCvGoSHDQxidsLpVeiD+aRLig/YobC5644baR4+hYy8A9eLsWHGmC/QjJlEk4WkO0YbVKZynoj5gYjYWoX0dEj2ALzpIc+pIKtkYNKqDYrAS88MTMMyPg5qfRMGwIaCYpA/nf4Lc/hAZQZQWS/GSbavSoml0M2yhXEtOFhlgAjwBn3+y5S9aAVx6ia+HUvpZMzRwoCwWSEVOWSbIiX4Eh5nu6F7d6DP5Ysn7S4q1fLlMgf2eApUEUphgAnGp/SqgLC4gyPKMcttkbWjAZMUuItYtFAX3MyLRCKKBnOwHlIzPwnbFfk6pvQDUpFDgZitaBZkUVbO4SkkRR9yZpX7sAEYz/BK3m+6Hy+md7Ujx9+ttRMdq0Alb0iHLB2iPA6ec0JI4f5Q1WG4gKMq6E2izBtYcWm4kBnUebj2N4OSD7shX5Qy/pTpW2fCi1iSqWrxPY5dSvlxav9lCSZ50Qk+9cFGxLsUgTE9Av+Ig2P+E0x8t1WLuYapavF4+W6bAY48CohgpEMFRICwQNXvKzk+r43CEMRLeDnJt4ght2CplAWutGMGJdyuKUWT/aVfs2V/ptxeAeimhCMPBGIEyKSWGLDFsTvFBC9qyKTbA5tuhUsZ8oydLJ5bCofGo2tdHEs/jqGMb+PYNJ0swjTAX0MqNHHoHyp8I504QLyJGzKADZ8/cK8HHAzjY3bSimBrL1FGKTGS3nFoInNXpX4DOeHKZ1Qni9JYmanyWdg4ecVqBcLDQDPqzi6wyHdKLTK2czATIeas8KtOI6nr5d5kCjyUKuNJ3CyFaFFVkUFaGPBzM9P2yeHA0UWO2Gid5UQSVW7pQFx9A1Gcnt10gEk2vpfizmNb+dLYXgHopOQiV3Pojd0vfCoyOEYuMF0UDE2NzePFv0J/DOpxy0uHIMgeTEMQN8uEQMVW1tBHtjoiuIVHAiW50Ec6FN7cwVPGgD4cFJauAfiL3CoKLpQXb/TU3A8UtZwZP4w7fCbjKtzUt44LDKO8KaMq+o2BKXdAZPlcLOFg2SUT2JsKQilUIm4eKaoSyocKJhKUtO1aF34+2M4bdU2X57jIFHhEKiKdYplQOi8BTVwOU4sVF8SAxXFzBGidiDxrLiMGM1TCeodFM8PnNCD2V7cYjUspHP5G9qJ4XKkuQpn9h6q1STKEtDVjo88ECgwZsYwpdTILzYjtXsF7BmRnEJoLNhzDxuPh1sOiK9RobUhy4BUOu0qcw94KDtqr4ixBtW4o/gqaJdIZSHbpykfhgTeHFLWU3uPxEA8ZPlp7V6LRoXpzpa8NNjCFs4UCNlCRR6KWbxXVmu/b40viglK0Jj89JKkwcu0bbIFCT36+4cNaBk9rysUyBxyYFxB1pidOh31Mjn+MgjAEjJvGsOxm8oE9VEEN8IVwzHU5xo4ey6rxkemJjSkOsgAOPBcrtFaBelEkTnCysp5kwRQmAKZrBNwQnT1WgUF1YqsA5mgEnMG4I+jRmQynvg+8DqnhIOKZyubQw645sXJbGAwTO0YNd4Gc6aLdCwws1J90HDPlEQxYmwn90juHQIq7nEkNPG+5jxygyPpzGRz66Gts2Z2LCTkB3RQxlGPtuHo3apMTqCxNHkHkvlE1UlSj/DpspSzVVqvvUPtnFNFoSGKEDnecLl04T5kpnlfsD1EkvtUpkR7OiU/U7ksZCoXZzIlYFHGlL6Vqmw+f0msddI6kTVygsEwcYN1wrg4tgdRhyYN4hf9LKOyNuTWXFQVNoWR4v0iZwYcyPqxSmx2/L/EedNe2m+GIVsUvZq/rz5dHzskzSF3dX35HEq3fYRai5IFXh4jXQPbxJTSIDZYYLnv3Ce6RDEM5V9axoIQYyC2Vl2YJOMZ0x0Q2wHGKAVJW7vFfSsspX6iWrOLZJRf/wdOm/VT8LZeHTkHZVRjHcqfoP0yz7bSiPgzhKWprog1wvpCnOuAIjY1g+T9N89t0cSbQF6fA6vPY1p2PN6gw2WRF2IOlp0IXVrDE1BAZoZIX6IHnuL7cfBdFHyTVW45qdkudyXYYhcpxVeYNeyIjCNNkOfhgW/AnLIFgYz0FmtdCJxK9N2KwcGTQ/d6NI509Kd55UzO6J2hBV8+gmM47qKLIadLIT1g8xNTeGZmMMSVzH335Y4+67U4ATjunAWY8oaqGw1CcsK1F1dBZQQIgnHKzl36iu6UIl6COCA7EalAsPHqETThxSwIdIr3LA9CCvCehUZeTv6DkBb3cmvuwTFciUalZSllH6MJ0AfkF0xPxJryXlqfrZgxTvAe8vfS/gRZn20ocsz27ujd5a+J43AwCPPhYnVryx2zZm+hW9dvlqycXuaMhXdpNf9WUl5F1woFU9WPL7UBPdohXZ4mCUNi7LTd8oP/NREY0eWxgggivQAs7ch8J+F6b2Qxx63DE477xjYYrgdZHVITiTE6daQuhnD4d2P3PhHpUP9hpQhxm56tUE35KDYkckmCrKmOmhrNzu89yYCw5/HIHM0iUkcYwNz+gcJb2imdAo1UDmzFt2nvAKfYD8Fw/ZQKwLiIpJsohtCAiMw9eEoehaDTHb66LVOBRWz4gTv/e+/S5Mb7oIH/u71+PAtSuQpwYF5xxTII4j2ZRmwAEeQRTCjsVSlyC1MMmUKkq7q0blgGR3z+ReCWZ00Vmlx2gk/BMul1xIRcySA6zy5/cLHNzuM1jggkYei/l0dS0cEdtssRwLHK+AQOljY6QM4dOSBpzQ2G2k6/DeElrwOyl++X4FLHL/gRzjImcYcgllLb9dKPMSeoz0rfB9AIBd6jlCs9E8qtVklfTiRFXekRUHxXKLeXI8VJzsLu/vpi0W6b/UujWAVZXuQnqST1Vf/j4EBDCqDI+l35XlZWzKYPq+uFpg32K/YddktJuHfUi9SYdQF5aZY5mb9Lk10PpwGL0ayp+GU085DOlchmajDApBCCmBOkx6JUhXkPOwC7FvvvgQrfRIFLpUw1vgpEhQglzYKFjIgUBCJzae8qUY2teF3hrcJOAymQ7qOfCWztDUnV5cNgdRB/nx8N5I91+Qey3k+bOccNNTAIM+GoIvBzqbZ2DVrFeHTzKMtzpIM4+ktQLveOdX8eF3cz/zyzj7V38fBx1chyvawYxdHDoFToB+tRcP9uxKI6QCHnKXBKPRmpB8IisqBxABtXy+dDCLaILphg3cwGXRDwo7ODdmSxSUfJlG9SdykVDnxQI+8Gy3E0UFBHydm6VlHZi2DPASXGUks02ZJ8tR1bmsCz9nvaRsQT4Z6smylX1BVAC4cVyKPFgn1lkxTNVS7no3o1ayKgc185eylNWUc+Zb5ifFDAAobwjNq4lmNO3R8pd1Z1osMx9J2Us3ozIO+O1oGaTiJT3KZ0vbtSxiEF2U35IWlSUsmQuhezWxVenzw1Kr4qdx3FX6u/hKqfJhmcr6VjRY6Gf0hsh6Eqwfeh6Q9xbozu9KesqEzA0oLgy4i8iAEiwUQ5JRASGDT3uwfoWURYYDyyL5sn4LFXhMnOwFoN4dnaqtgrJ/loODdxn5mtoVjEottKY7XxkoDMO0mBbPZdxIQ/E+Yb6SSY2k/19tMHH6XirJSXy6clODkUx8AmoAJc0hsqILVRwEZXpIdAu33XAMckzg0//3H7Hm4AOQoQfoFrzzoJVl4TUiKudXVRIaBKAK3HU10bCJCFZ8VgEgCcE/fl0OREkodFDh4sqltKYzedmIEYQoOXeuE0OdKo4sSFyqPAhwD7OzUydcWqIqG/MZPQicYcMnlFlarZxwq0mX96rvKwwo60jADUNRfsNEzO8oqyZpKHvnI9KoAk0CVh9K0fBoV/UuSWTkn1GuWGhR9sXqFS3xHikfJTPAepRzpORId6HVm6O/izelJ4qfaLYD0wl1JtfN5TzrwfvSf0kC8d3N8nMZz+z4PttiMc3RnIKVbNk/ZR4gk8JJLKTFeJALG9QcV9JvwuNd09n9FcOhLfa1ECkm+L0u31dz5T4D1WbJbHHSZD7cR2LfXTJZLs2G5SzLJH0gdMTQnhSAlg7bcu8QmVJBQKwOa1CNFiLZwyn7UdX1JM2lGe3f13scqDkQqqVgWAYG4xN2pDDAKgKye7GBObBDp1ygu3RA6cvh5dGGkEFQ3V7szNW3VeqPyC8TVQ1hugiPLAYne1vQH27OfRWkfYWkA8T6YInm/cTjjoUuKGqwEplG6MFoKOzUDIZaFVS4QKY6AlgstPjY5n0O3vKZgHDwJBacwvO9iigVDaqEqVtO2T+/Dd/LQBbORdam4X7FEcmgkYqWnF+Vzu4pKGGfqkcLhjxVHaj/TnDjNTmiEkyl4clpU0uGA5n3yzKyjpW8VuYTGjywTuyq5URMPwILQVkZxJR9ZiDqkrD0yGMBCQ7LmI6Vd7qqkLv+sl2qyWrxCYGxpCejvyxwiFwdBg5ZCUAQFKu6VnQnB1HdY9VC6DDRcuJYKOMlSmBa6hCLiI8dPF4IViAbcZx0dOl1T8CddCzzWJi0adTB/Y+wEhWglGg15QqMm5UyefGatOPvknG00AEXaz96Jh4QJd8wSUlbSP0CpxTIwYmiBGkmT9pJf2LXeiiIIZ3LPibV4z+kn4wuDKkPzVBiDApQ0npUQSCSdir7TDUERivwGDl/KCrugWou9hSh8QIOsIG4XKo6I5sqFWfsHKcSwaV6lyuhsnFFrW+hlIG7rF4Ltx9iRl/49kFOpDxOdtXJUTFtjluCgdcWmZ0DhgeIK09ttmBF50C8+c3fwGc+uxOmHaFvT4YyB8OoOiwjbhuWuAdDXVBwdzoY9VRcVdVpA5fKMnEQMFf+G+pXaXHwN/TNkmZyVZ1Xv3yD4hVek6PjdSkqkkm0crcZ8mA+ixwY6xpyeBDqhECw8jCAk4SyqgZeyckvcnEsQxjU8isiAObI+yVXJM9Z3YqTbAVudlSWGpEOqYigGDggMFUlZ89VimoAigDdguNMWh4Vw1Bd81c0KWTrOpRN6CR0CW+x7BIrgB7bpCMSiEJbBMMtRlQJeYQ2ZDpMONyTKwkMUPZY2VzhxMQ/ir/4Bo2kOCsFcYGQZUFriemUf2XHHvVxLSHo6LKAhRTUZDtw8qFsn8EYGFCDcjVq7QAAIABJREFUzBFXqqFMLF4IY8V3fzoEOJRRduSjoDlUMVK8pRkdndVdKNtCaeU+vaw+9FGVq/pd/Khe3SrT58h3dC3LFYnMFWV/rRg/krX8XCbhhX710KXYl9/46a30iJS8onSVWDVbltcVVXcBGTY8OUjKHQlG7GhsFaYV0uM9AbClckgmuzTLKuuf81c2SwQNmHAJKORkMERsJpFT1OtqSNPtqLeBvDcOrTfg7/75PDzhCUWIa0DrHF2U8fSCTNUUbcEFch/BL3CQf1dV4ApEy/KdNGNIKP6yeuy1IVTWLpylPCWdZGaTq8AxsufyfuCKFgdVCRYCqKXYgAY8Apz0/cwVQ8hTEtvtP9Wg4m81fVYv8t6w5ORKjpp+l6Ue3DBi4GF2QX5H8A1lWAAUH0mMRSnPCBCQ0+TmLeMikhahudkelkmF9pcI3jVokc2X5WF1l0w8YnwlQFZODJwgCHgLDEMOzXMSTbhPFoScNDe+2T8J1KFw0icFiCuaBLJzUlGkg7zG+vJ56D9SMtaTwBoQuqwHd5254mFcuiAGkfaTD3g/yORJG28rk2u61w31XygqgVjKXw0K1oPfMn/Se7xqrN3+mmozsayljEtZoQWmIdCJGlmhzsxF+qcUgBFsRmix2xxGbz7wXVuKs5g+NaZoyMZo4zzYJF7al43OPh/qXnYVNvZo4vv1+R4H6lFahUFScU6jdCPhy0YqaRu4uvaumDvSjqFNSlncaFLlOF1y6+e+lCWd6O6y3ANRxicogyGhyPXkWVAhdB71zjTuuX8nNmw9CYW7Fmtb18KqU2X1SzNz5RhfkdZTFt46mGgKvuRICDKMFyhBSN1Y6HNSX/rXZuAD+i/hKrdbcgwa+SBCXG+KjrHX0yE0lZssuaU5mVS4VC/cLIzI/RmRewBl5sVhvcupDz6E0gQSbjbW4PwEFCN986BuroB+Rb4Hgja5ygDsBAx6E9RwKSNfc8mvgZgaDJxkWH+KjVolkHbh9XYoTMAN24BNoOMkSEg4CJl12bwW98JEfGel7DVRLVLF5O6mYdGRbDRaKNCHi2bEayEB1HHpzwCzmJb6MW9u3hLkua7RIj+tI7XbxSUu0EGRJxJ5XfZAnEbGaN4qgzd9EeNE9MxI5oCF40Ysg0TwCCi84PxfViIi126KJa6yjWAOrbfC4ebgqtOvgkUfRlGOTjPbKIAu5bKiucSgBP0Qh1LyZLYFGMcxMC9BM4rWedwrsZbmIx0osdzLoHQP0sZmCKemoHRf8rUUpzkjIgUadDHvEECBqxD2cwY8CHAn7av7yJFL5KRCghhPQ2NWosQzDF1etJFEB1IxS/qt0YzEHnSad5FnV93oZ/gdtTAUi/CRb4kt4n1v5N6upyOAseuD/e5qjwP1fkeRJQWWyYV9VjbNCEqcyUMMOi47LXuLSQS0bXEALr5oEy7/vxFOePJROGDVkdX0U5q5cyBxdVArQYhX5JaYqoaViCc0DuGApEBgKBwi3boGTpEbZLxHoNDipEoSYpkVOWwOsMA1Mw/KPQs1jYibmDmBMymDfioBOs0grroDV7DHR7A+Fp1wMoh5YRHTg+HCiqciTAXWYRA4H+zIONFYl8pGqU6YN98L8mUCf4goHsRQwglRk0fTV3ACMS7TlFfPhpUFwZB7AeQtcwOTtEU0QCyUgOMuE1zUinrp5ERjZN4ImMc6LMULNxCsE6ZU14XnFWBm28k+CFPnuUY9mhQxinUWcRJiOFKN0kTSwqKf64Rz5jeB4/aUNTsCZWk5qun4ngXk/6W9HV+XvtIRUC/SFFo1oWvHweXzMsGaZFyiyojyjbFQdBnA/+ltSJNTpuiC7V1xIBTDVauI8l16kvMWptx4C9x/UJejhS/7CL1TUpWUXCaDD4kMWfYIqKfOtmTbVNx+1dacqyNkeQdxTWGYO8Qx+2sk2k3NpIXCdVAzY8LtMtpNFOdhUtYU5ySPJaZ2kSiPwtkyUD8U0aslsIhiuFzkIOEANcJhFVEweiEnrM3hGB+nkKKLN/3Fs3HiyYHDCBtu1QCkDJXjhWDGE94nZ6aFs5JsOMDJxWEOzsbQoGogB25XuDu4RMCVAMdI6yGXcrefq3YOcgEh/rsGaR+oJUB/GmiON5BnJngs1A6uoLOoFcIJ0xbfcRlNRjimNgI5unAEURTTDYgRxBcsc6URkcHEfeHU8yGdVRUwtVmQezOqCUdLU1qWGk5UtFakuKcG6yMJvKBAI6JtwagIDN+lAWtgfSaTmxdVSIo2DFxRl01CHTdkhcIyJRKmS0EC1LP8qiHGSCyuFnltKc4gloq4hTQn98jfhpRJYT64MiAwEt8YoFj2FRMYr4NfCdZXZNT0nEgwX9wDCSIp0quazOgoiBuGDQmaSy9vtjBIu/SdvAJRwiDIpHcA2qBCSYEe+0gZbZ1cNkkuB52YhTYIqxiZFcAiMJiybBj7GJartZgtx9mmjB1ILQxLVdcw96qIm5CMwN5ZBNOqsavs+KuBiNwxUjTi+dJV/xia5nFApiExZCkON3MwZigbfgpcqXFyLeeA0fSWz38uCjx21gY/V/Uf/kccBKI/DZq0c+lJXeQgO2SMRC7Hr7/e4kuX34gmdmIwvQM5RdK+C+v68G4AZzMZuIKjzFrG8zyy4RxcTrN5JVGzhTHTDgVV/hiVW2SClFkSlDnwys3AUrYsfg88lb0IhoQtGgQl8kegSmos+wBRcw7WzSJuZnC+jyLX0PQsSPmfqFZTPZLgQhkmIYwgVJpqi6y6QgyCSTgnF8qlN8tIVTDKfIORAlPoilkvVdvIXdmCXwWNDS69XdEQ51o0dKK8VDaIRARgAGrKWK4aKK4h0Dnkfhty7ICpW1jVl4mLFebEVOTBqlVHFA3ksAVlu2EKE3qKCTK1KJgXiUS5M12uaaT9DL7wMD6CzYLsNoocvC2gCgPkGsgMlDj9yWFJOzeAZ5kKNfKnUViFwvHPI7cForgOYzzSvAuLobhH8FohakbIfQ805uLEwT+eE7hFNU82rit6szFG5dJsF7Z1hDyjST4lJURgnlMMEsSLvX5P/OgUzJcxKOmTRgEZXQIT0EWHPwC/yPelbcr2limaKk20qKXKBcVhbQzmY6AoHZMlXaikB1tsEncJFCdxIqJqnuVmb9SVfrT8z3+dAssc9UPRkAyXACr5Jco1uYlSbaQAZHDI9RGgvve92/DlL38JZz7rV/Ck4w5Dm1IIPwxiEzqXoqaIT8rNqcBNOz8Nxu8i4PmCWiXBwIfRuwmU2iQSMIF5Mq65bDCSmyegkiMS51NKPIfxW3JAwhgR9Dm2uYcX9dDvb0azuQLOpRgUM4iiBjwmMMgSxBE5Xo0sy9FipBrDeJZWvAGK5GMEpIPskmUL2iicGHjO+JcEpiSif2CKBzSGNkWkyR3XRXODk4cVU3oCUgyb0Xc3Jw0CuYYx3NiitSfl0dRYKMTRVZw0MExn4PW8yEJFlusjxtKBzbqo1TrweV3olNSp004ZPFCvkavmZh+5aaqwBQ0XuqIVQjnKz9kOw1IHn9wr1eJmBcidi8BIIY5zKL+gvB05Cku5sYHRBbI8iGhIA9IqDCjuN9Aoiv0kRY5p+CiFMgcgNhNIhx7DlL6TG8htF04HLjjWESKK0co1ktBa2pPpUcTCNJkDxQoBjMUHc26RF6lw4dQ1jujfXUVIkiZSFMhUX1yBRvSlw15UUGTHfY8Ysfix4WosTL2yN8ROw9UG5wlDTpzzdYK0BzSijlynRR8m2i6WxIrtTVGQGoNBDekwEnGWiR5bUVakYR6lf5aB+iEILwOO3A0dAyES73yiCcFOLHuJ1MwIPkmcPwB0ynTuK8bwxJOA3uAONOoTsiMuakpewVCjgODqKB4IMuqEgFJ4WAJbxA0loMgVovqEcGYFQdN4aE/gCepIBI40T5E0+sG9qmye5fA6l51xbnh5myKzNOXtol4bw9y0Rau9DsavhssidHsWEyti9PoDcRplIsqZCUBGJDKjG8GE46CLHcQ0lZRUqySYxdO1pOmIHLw/GEKZFHGd8tdBEDUIAA1gZcNUixm913Vo5LCW5uwRDF3IconOAKYEqyiXlQj8pBiK1GKFNJ8Tjo4+xoeZQz2hiuQMsryJJAobffS0KFF9uBGnh3A2yGE50Ya2C2IQAikZ67ieyCRM72sm1nBqiLygy4IJuCJC4YLsmIGTxZ2BiCYosggcLCcC0kprDc2NNJJIJoUCNmcg1g6UG0cv86g154BGgQFBr74KsQnm0llWIKJ8ihOgrFB4Ri0OtjRXAWGVQ9pIpPSS2a43CfKkVwDFwhXIU6qvMZ0arKGnOTIJBHLib01WQKL1zL0SdjaRtwXLYFH3k6m+FOnQS53Ppf8ldQcVR5iZnkdnooZhFiOKCsRqFfpziYTAarQVCj+H/5+994CXrKjWxb+de3c6ec45k2cYZoYhDkoUEUFAJItkRBS4crmiYkIBUVFRMSNyFZQk6kVAUEBBgkgShHHIDDA5npw67LzX+321u2cGTH/De//35PT8znT37h2qVlWtWrVqre8Tw0AMwo1OKuu/oWL+P/08qaj/hpgIQs7OS59htsOcpbIrq5X7iA4VmI3H/jCGb39tPbzExYa+F6FjFixjmhogCqdarb9NGLqLhIo4TmHpFnS9K4vaIH6IGSpDT907cRCFhKMRFS1GzRnTeqOiJ1kBY2UNHUkcwrYyJRkpC5F+RPo3baVwONZzVgkp4VqtYRiWgzRuU37hFjtFFAC5nIsgqCPvckMyy/Sjn5PEB5k7InueUtYqiJwlVKVUYzyJGgQQegnCuGWiucGCgQ6E8Tg0I4FhMGogUZx2jGBhhFVqxoijGHpKl4tafmSzn8pC9gDC25o1xCFxUixoTgiLlGqWjyDU4dplxL4OGpCmnW0eJqmOKMwpy14jO7Vez6zmlG6eDKOc/nJlsZOhhxt4Ks6cm5mh8j+rCAhNU/5vuq9oJitqOCpiRo5orlLIXJ1Q09MWpWWezd2sf5qtkLjpqY3DdjqAOIHp1kE3RIocCoUOhcqY+Dk4ORquXD2xLIFSpMzQVbMI5d10UCr3DTssl0r8SxCECeIwa3/TJCKiD27qxfQfa3lFBm0ygkRhruhZuCMjgFKGPnK1SKwOuq7or+bzOTHwj2Y0lW2g3HpU+so9FoUot7uIGTViMT/AQ5gA+bKFyngAK3JgMUJHoyusMbn8jTE2+fPflsCkov4bMmrGLrPrclmYhXQ1LD4h83kISxf0berC6pddvOvEMk4+fRYm6oNwSqYK91Jp8QlhWjPXBDt8EnOJSssrh4mJUO2q5+i3jJiJlW34jI7V0NrZpbC5w8hXS2bTtKDRx2mkcBwX3lgbrKKbKeaQbpioES7tQlILphsiCVsxPuajvaMH4/3AcYfeAa/aBc2qYv93VnDeBUch5+YRJZ6ygahETRZCLbcbXUT5dBvLCKpT5QynnxdwHANBCFQrPkrlHIpFG3EiGB4eRynfq1Q6oxpoMYdhXvlHTZu+TuqvCmyL8c4UMLUiscpZR/p0RxFiLVxrHvJxm7JCLbcND943hgvPuxJRUIatt+IL3zwcbz6Q3Hm8F8PrHGXl6wYVKZRbR8Gqqnh1Ft5CTCVFxaeFCH0HjmNCt+jWCJUryKZvnGjgAhUJYli0puk3B8xGJAz9yVwB8cV5WG1NNhQ2swXZzm5uLpJA4HkRXLcTt1xbw/e/uQwmk3bsAVx+/SLMmFVCocDOUM+AyRqsR+BqiILhckxt2tIo4LTASTpUylfXHRgGAb40+F4IEMLANhRCI7cf0tSF2Zg845D+fJaXrjK6gqigs3h5tUnJSqjppjERsOclebVhTG9IxAWK5SAMNBz/zu9h/fJumOHuWLx3iK9cNgctHfR7V1VkSxCbYF+dfP1rJPBvr6g5r6ugOLVUbIRSsXMqg9CDpIxvpYJj6BuR9og7XUSU+LANUmbmEXEDRotgogQjKUPTxgCjHzHKyGkugjCEkyPj48uYMeON6OmuIpqgC2MEujUDe+39WVTHdkbCzaOgH8cdvyfedug0nHby9wB9Hi6/fH8c9LZtEMR5pPoIJkYiHHHQdQi1hdjgL0Fv67Oo95dh1eahu7OAteEyoPwKOpNRlPLbYv3KEey8Zze+95MPIB1tRYGuXmsEaU5DEk6BWQHaLQtj4xHetOd1wMgi3PbbXfDbX43hQ5+5Gla5gI9+9A0QWowMQ6NlKfRRO0CSKWPNGAB0yseFxAyFS+F7dL8IWl0N8Thw8JFPY7Dmo7P8MCoD26Bm9GCB4WAw3QireD8cWlvuYoyND2JmawG33PEf8KMuWJ06ElMQi8DWGNOtqRD1UKvCxRh2nH8+5u2wGDffegSefHgYxxx1Kd5+xG749Kffiks+uRzvOOERPPyH3fGNS7+C++/pgR64KLZUsd28Em79+Wn4yc+fw6fO3whdnkNrzkCiF1HIT0NlfANmz5+Jm294BwaqHjpbIuSMVnz3yt/ji59/EGZ+BHf/4nx84fIv4ZklZdSGY5V5WvHGQJzkNquNjhukmomx4aX44XcuwH5HzEAoOTBE0eY8MRFjXFK0FQv46VX342NnT+D9px2J936sjncfm+Dt+12Nh576EOxCDJ2zAELYOiOCqJA1NXcROc7KldTqx7ZiwJyAJzG8wEU5TZBzDXzzsjX40pefwvU37Y7Hfn86br3mTRhM5qKlfQ0wnCKnF2FLG9KojtDtxIS3Dl/66L444T92UpEyQB66mhT8BqxwXuns23/9Cj549g/wxUveg9NO2V5tlp58xlm4965O3Hv/iRjsc3HqCX144vl1GA1vRLFYBMw6KhvW48ZrPobd95/6r9FUr/e7yL/5KxJPUtaR/zX/kq0+S6gkEMUiUSISq594QiJplJ3HY56IOjONE5Fko4islSitSOiL/OGPL0kpd4UUsFY+8am1EkkkfjAoSRzKpiiQmbMfkoI7Jqa+SnSslqMOWyU/umOtGNq9UkBVOrWl8svbV8loGMv60XFZ3y/Sad4nFlbKQQcOyo233SWFwqfEwGr5xAdrsm5gTF5aU5XVmwJ5canIwu7z5A2L95VUQomCUMQXieqJjI4nkkifhMlT4oVLxJOlsmFApK8/lVQ2yd13PCM6PiAf+uj1Uo1FwkSEclB1T0RSVjVJJE0pukj9eYEvQZhKkvoSBiL12JdkVGT/3c+QkrZCtt1mTH7z6HJxnQ+IjhVy4rtflv5+kTWrRdYMVmXDOl/eMOfjMnv6m0VkQNIKhVxVZVcFj0dF/JoIDyeJJJ7Idi2elDEob9rxfhmvVWV9n8imsVUi8qx85ANV0ZyKPPB8VfZ867fE1PqklOsXXdsgu+66VrX5ly4bFBihaOiTVnud2HhBgKWSs58XA6/I3gtfkL44ktFoidQqy+XCC6tiY4XoWC6/vC+R9cNVedOCZVLAKukoPS7Prx2Q4RGRl18Zlo2jdfn4+d8T19ldHrj/OQnFk3GfNRkWv7ZeREal5k2Ilw5JkIayob+qZBxGiRx99NdFx5ny8qqKRBIo+fLKJKlJHE+wGUSScRHZJKmskTQelnCcx4clDVep75KOy40/WiNt+lJp0xO58vt1Ga0vkbfv/ZBYeFJs6yF5/I+D0jcyIi+sG5LBcZFvfuvnYhm7yU9+9ivVtoFsEJE1Ekb9EocjIlKRVOoSyoTccBXXfJE47r1y7U9Wq/Gxum9Qlo+tl/XRJnmp3xfgq8S6FAObxMCAAK+IqT8md99T/TfXLv/nqtf0fv3bzleGWpBmIUxqVUe/mc54X7LMsNpMCajANLKYYQrE80fVElNtpjFxgmzi/OP5WqpYWZQlxY0SB9g0MAUVHzjyGMHFl0zF2MQQdL2gsrds04aX9MNPaL23YpvpLWjtyCPwLaSGAx8uEqsNRx3+DTzzYh3F1syCn1ARIF1wqm047sh98eVvfghafgRfu+wu3P2LADNnFjC9RzB9bgV/XPFlPPzI7UiqFvo2Wlg1EmLY1+GWaKHZ2DRewgbfw8o1Ljq6BtE6hXmIPRga4a6/gak9rcjRL8zIP7XD34j11iPE+oQKKeN6gu4fWtmGESL0UpU8Yxohc2awfsCHLx5m9WrYe68puO2Xn0dZS/DTn7yCH175CrpmAFM6gWIxxCNLLsXSl38JT7owNGBi/ZCOwTpjl7mZ5mLUy2PdILBy9UrEuRBuS06F/uVapsPJR+jqBjpaujE2pmN9/zrknRQFx4Zt1dDTXkbo59WGm1WeocLR3HynWhnoVitMN0bPNBOtrdMQJtORmnk8tSyP/d54r0pwyRdzEJeYeQU4uSKqUYLO9jxuvH0BFs7S4Ffa8J7DlsMLxrHtPAelvIsLLzweqzY8iPbZ87BuKAcfE2rT1My3qIgg9rf+MQsvbwyRKxVQTT2MejrGRmuY3hMhZ0fKZx7WmJ1IN1Aui1BhFGHCjc4caryjZiCl/zeNkDIjUkWflBHqVdTSQaTpBErFEEW7hFvu3Ae77uQiH7XhpLe/jChuw6zpLXDLAc4+82gMDN6PnXc7BMvW0QM1FX48EwbdXZaPWuphxXoTQ1UHtThBa6kGLbTh13TC96M0pQC3mKLFtLFxnYNCfldM6Y6xzbRuuHY7TGeeSoSpMVFr8vWvkcD/uTnh/58nKWua1mCcSBwmkkSjEssqSWSdJFKVOKSlOChBWJUw9pSV7IdDyjKNaSrKmDIzw1ogCa3VyJcgWidBukmiVOSZZaEU8Iq0Fh6QT53/M4lDUZa4V48krAxLXUR22uUmsVGTRbMTGfUyY/G662tiYVSKZU+KqEsOv5cf3LpEVoejsrbuSWvhD6KZ/XLUPrRsxsUTX75zw13SVrhJCnog3/q+L9EwDcZh8eV5ERmSlx6LZVHr78XF83LsiWtksCbSP9Ynb9gukDx8WTTzXlm/IZC6RHLdTY+Kix/IVy5+Sqo+rbm6+LWY1ZOxYZY6kiAYp1Ervi8yMSLKglZWngyL52+UMN0ko/FKWbLUl3ntj8qcqf8jnlRlyH9KqvGo/OLOV6Tb+YOUTZELL14rtWhc/DRWxnIci6xcGcrC9tsFeFLefPBy6R8X6a/0yYFvf1YsvCxTp31QXlo1KANJIIXS1bJo0b3y7OoxGfZDGauLnH7EJulpPU9+/eSSbNUko7Jp0JNZra+IZq2Wvd86IHEyLJf/NyO5Y9GdW+THP39R2e+77vVtcdwHxGnfKA4CWbyLyJq+MZFA5MLv3i4w7hENVfnNPRUJJJRqsE6iuCpv2rZfcsadssfi38v6tSEXL8ryPfmou5TcNQzJ8+u4AotlyBtXK45HHghFx2OS00QuvGBCNvljssde/yNz226SdavZ/0RqNJwDkaDC+/FIRfUl9icvFRlJExnwRWqJSCVeI/V0qVSrfTJeE7noorUCXCOXXPEHJV9Jx0TCVJXt8F1Wi46nZHrP07JqfZ8EqUg9ELngw/eKi/ulrKVy/+9FKqkvg/7LUk0TeWrloDjajySPmnzqTJEf3vQrsYyL5MsXjcuEX5fRmDIR+f29VSnhTjn1hEelKlXxxZfPXvKM6Ppz4iKQW+6ctKj/VVrv396izqYzuuK5Y09/qwmdmWcMsWL6s5EgjpmUQCLdmgrfYpovI0vHR+lkLGSJLTGQxgliWtWGqaIDIu52twcIYODt79gHn//isUj9cRVhkbPpo86RlBxRhb5uA0ZaRz43DpMJGcEgLGxEWAuw7aIqZvbYOONd16F/bQpTaOUPwE6mICAVUVpELHW87+RdcfHFB8Gy1uFj/3UvbvjJaoTog0pnFhvbbm/gc1cW4GENbv9ZhF//HDj/oq9hyYsPqU2zWx4+AB3dgltufALvOf4SnH/hwfjIBTsg7wzD9zJGZ9NI0NLmYnR0EyzbBtJhGDIE106gp1Ukgac2LHXDYVQv8kYZxx/6NPpGXDiWDy0poGi3K/jXQ97Rim9+Zz7yLWvxpYtW4bKvVlCTOkJUkEQBZk23cN0d+wLox0P3VnHNVeP47+8/iXvuekHtF/zirkuxzewcRtYGyOu9eOGFOj72gaeR6IIP/9cduP3eJ/CL2y7C/m/YFeL3Iar3wY0NxLVRCNOe04IqB+PZbYZEMhA48lFAAitk2FwLgmqIvXazsPSpR3HGcTcB9pDKOoXNjTYLrVEeEo8hZzMKpIZbH52ChQttPLnUx7tPWYH1q6qKtGD7vapw20KYRjsefbiKMa8GKyeoRII/LmXiRwlT5o5jxraCD73vDqxfM447Hjsa02exbzKcbRgxGJVTV5mq1do4Un0MGoaAZASWxtDNFF4QqMQhV+uG7XTj4XtjXHqxATgefH8DbE1DONoCv0Zsxglcf1cP9juwHZv6Ahz1pmVYt3YIjgXMWzAFU6caqMsoHnpoo4p5t5x2TIQRHv+9jVQvoqVcwQ4LagiDHkRaBy64+E4s+V0/WnUTSx6q4aC3XY1jjizhqht2Rh5VOIjQUSaWzXoYmo0Cw1kmX/8SCbwuFDV3yglNycg2XS9Ck6nQpRsaoxAihqPpSPVVSMw+eBhUsbKM/21v60YQMO03i5FVu/08l4Mh9TAxsQm33rgKLmZhaD1zv8aguQRHegXQ12Rp2GaESM+jjvXwzFcQYRVibQR2+xDq2KDYay75Xhf2PvQlWMkxuP/mNngV7uNsRErfjK3D1hkLTQWX4L8+UsblV05gdvkgfPicAgbWLoKmFVH1K+Be6PwdNeywHaMperDk+ZVYs+ItMK1xbLPLb1EoBbj953346AkmvvOln+MTF0+HqXAj2mFpBbgFJpkYmBiN4ZboHBqCbz4KyT0PszAA3arCcMfVRqNltsNIe5GEHejq3QEhpiENChBm+UVTEGEEdbkHx50Z4ic3r8PM7l58+nwbzy7tU5mbYdoH3QwxrX0cb1w8jlwyDS89Y2D50umwsCt23nEMbW6MJI1RdEuIa70oGm/SlvxjAAAgAElEQVTHwPqd8aXzl+Hh+/px88OHY/E+3OBMkRgerLyNJGTESCdgt6AeClJtJapYr5JjiC0ShS6QmEijGQij7dHTMQPX3sqd5T8inDgKzz6rwbbGAa8KS7dRq+twJISBfvicFHODeHDpW7H/W3bAbx9M8JXzlyKKLHzkEwdjj927oSWC95/4OMZGDMRJgJdXrcMF5zoKF3rRHvfi8d+OYtUTO2DpK6dh3nyG/3AcB8jlB2FykjACFcNtOTZMkrRaKXJmhjTiSgI39VDSmBnYRbwrmDnGMU8FwgJ0Z5MKr2Q75coGYk4w3cP4yZ06jj5yNpatSXH6CUuhazWcdtaO2P+QN8KAjS9+Yg2eXxIhTCPUIgsfPNmBI7tgu71/jhM/WsDyVxheuCdMY0/4toWHl7yIw/ddhtPOOBD/fdubAGMjAmyEFwq8qAdwelGTIcRRlpTzL9FUr/Ob/NtHfXAIqhA7wpLSGk6ZacVwJwNiVGAw00pL4Qezcddt/Tj0uE4VO5pzQvihqeKiXY2xyIwY0RQpraSkAwK8eojzzrkJczoFRx+7E/wkRN7sh520AEEZiREgFAcqFA1FpFEJQqaXpMj8NsBMoMcFbFoH7LyXi9/9sgdf/eQIDjm6A3lze0RmhEp9CIIWBDER0CyEaQ277rwYNe0HOPzYY1Hu4iZ9N0xL4EmC7RbugBtuXIizTurHt7+8CbBqOORtC3DtTbvAySU47rjDMMu4Gmm8Cld8eQSBX8Vue03Bm9+yk0ozjwIXpmXCMgu44bonMJIU4UgHnFgwfZqDgw6jIuxDAv65MKwc4JB5w4WdLEI0YcOZNgGRVpjhXsqHP3fqHmidch/m7vhGLJo7GxJMwHVNBFoF0+b24OZfHoF3v/slXP8jRntMwb775HHVjXn0dlZUOrqKhXZWIwpn4sVna3j2qQex715tWPLHW/DoL+apJKQzzp6GUkcNdqmOxFwGBHOgE7cYuzB7KMMX0QOVlMK2s5gRqQ2qmPM0BI45bh5u/dkALvtGJ95y3M4qnjnSEviOgSTtULRPuh0hXzBAsPrtdxvCEy+uxMFHzkRg1BSyyqGHdmPZkgGM+53IFQvQtQ3omjoHdfhYvMv2qG1ci2sfuBEnHPIO3HLFc2qPolp5AWd9aH+UW0h4HEFnjLbJaOY2PLUEePzxFmVgMKwujlbi5JNnIc/QPo/LBMZrAzVmUzozEdcJgpUiSoqoja9FS3kavDRBh2ljp5193HdfHse/ewYk5aqrgv0OquLe34xhbIML22qBpY/BKrKfm+hqz+HM9xyuIqGd4gRgEHmQ2nIazjjrPFRxKObMmYtvXvIwgqAf2yxajxOPX4y6LANCZuoasBVu+KSy/lfMMf/2ipodlyDpwiw1xugK06ULijIr1foheg90rR1f/1SIK7/ZildeeRwfv3Bv1KJBWJrAzNvECgMIGhRYUIrM4DLfVQPG1Noxd9HzeN+5O2EiyCFnLFRKHEkVRqEAN3UgPiGDWmHFDvLiqQxDqeWB2EaIGJVgAu87/Sj88GJgCFX84LIhiFaCH+sqszFAiKLTobL4Hn8wxLsOjLHrkT6u/2mCYSbF1A2UWmKMx5tQRozt5vfi5DMcPP6Rdkg6D5d+dwo6CkAtTvGFb34cYy8PYsNLRfgpiRlMzJg9opJGGJLHVYebB+peOz7y3hcwinersC4N4zjqwHa85QDAcauoRgNwzG6Vet3fz2SddnjRBpQ6toFHLAhoKDgBlj/ahUOPWom2Bcvwi9t3gm2OojLRBddZCU8KcFITvdMs/NclJh46YASJNxfnfKmM7qkxJOzEWF2Q6BrsYoKxfhMLt9Nx5nvfhg3LN+DZx19Au+ZiaJOJodXboatrCvxIh0YgpLQOVzHoCOw0A1WK0hQ5q6p+M2VCxflO1Icxe2YPPnvp3rj1ZwUsW1HF0HUEZ7JU3TyrjsjJQ8a3gWtq8BINp551E267Tsdtv9oTBx/chWrkgNmjZ56j4+rvvYCNL9r4ziXrcNGF8/G1L98MGO/CvPnA567oxhWXDqIwkeLFRxJYnQ7qXoyw1oHQaVUrMCZDwYxgwMI9t9Zw3hcJs0oAMDqansX+B28Lw6nA1BI4Zoh8kZMkNaitGINonVut6yE+eQUFjhbhM+f/Bl/9Sh+uuO4YvO/kNlTSGqFUcMK7enH9jx/CQ+u2wY+/M4qPfKMT37rqNhj60Zg9uxvHnqDBiHykdfI7tkKiqXDDFBec+R6semseG1bmEGET6t4Qulu7kaR9KpEGWgtK0gZtEurjX6Gj1T1eB4qa3h2mardAtDyEKb78QxGmtCogpEADWuelGDbuxGc/PRuuXsBZ589ELQzgsiNqLQDjfdMqTDNCYlgYmRjG1V/qQCJzsaF2HyKcgHbCHUgBdVkBx+6AHprwLQ++60NsJgJo8MRFQQOclKScxIyI4NCNIB4+/7Uqzj1nNS7/bi/cYpuiBtBozKAVBgbw8H1T8M4jr8cB75yPy689Hb64iFKgl8ieoYV8PBOxWcPzL7q44WrCaMbI2S/h/HPHcPk1C9Be1nD+h08lT7BK7oDMUSuMKGX24ABSyaIrONP4lQJMg3jZyxAwNwMefOI+GG+mhxe6VUaAYZRC7vSPqXIYzlQEngOUlqNgzsPTv+vFYafehgU7Brjhxx9SmYk1L4HTQkS86ciFFiLHw5p1eVx5fivgDUPX/4D//sw2WPTduZgzN4HJBA4jj+qoDxMFbDMnxYc+3gUj3haJeYDCWvH1BLmqocQJN1QWqRYIhDMO43uYRs3Aefr6tQpiw0ZqdwKBhbLdgdgw0GaVcM5JVXzvJ6sRmDYcuxtBGCFvJDBlPVKzByOhgQ+8fzl++aO1uP2Xp+Ogg3MIwjqKNi1IW+1VfOwLu+Bz58T4zqUv4KwLp+MHX30STjIT9cFpmFl+I7715cXKaNBQbiDRzUAcMJqGlrsH03AVPVmQ1qEXnoShDUGXFrUSs8ynABwB6BPI57n50QbTJyATk3GKSOMewMvBc2O41lSFb/3Rj6/E9771Eq7+n3fh2OMsDPqvoC23rTJaiDX14XN2w5rn8vjutT7O/oaGKy9aBS19DrUwRiA7wLR05HMuEPVDTBObaj045f0HQEtGMrgAbUfktQhBzIkBMIOnFJtRhZEvxQxy9l+mrV7HN3odKOpm69IJQvxhtX5rHEwRGQw8y+OsszW0tCzEh0/ZDZ++YBxD2q/w6U8dBuI/EMzGMgDT2gBBASOJB7dlJr522XosmrEfvvCVOTBRRxhxwRdng43LbrMEi+F8+hpE4WIkEVOnMyClqFaGY5QQpYQd9eFoLt5xrIH3nvEQ2ksnIvR6Aa0Cq4uoeDUs+UMrzn73QzjqpAIuv/INMFJLoSjEhQgnnX0sZnXPx+fPvxSPPRbgP097Eate2g5nnTsF69cN4zc39+G0Q4DrbtsW9pRhBZREXy4R2HS9BkgNUciN1ryiCKsH/Whpt/Dtq07BRFyGnRbgSA69U8eRM2tIEg+O1gYvYChbRVn/wCCMQhUePLhmD15eA7zn5Bew+z4Gfvg/+6BI33/ShWJOw6fO+wb8ag3f+e75WP5yiJOOex7PP70bTjqpC1K+Ezf9oB/HHjYXt/zGQM9cF7FPlDiHpYVELrx4AIVkmspUNHMRArsCQy8i8ZiArSu8DpIMR/qoAgoyLStzNSnHgA3DJJ5yBZbGtHaCKhXRNXUC7zg+wg031mAVt1MEKAGfqC2HqY0DuR68/9An8Zv7B/HYsx/D9vM5nQ1Cs+u45+4qvvG1O3DRF07DMUd34+sf+z3ajBZ8+JiX4GonYta2MT5ySYdCk/NSYlJz+5kp3kzN16AZPmKVRKRsClRrQ3CLIQ4+YjZau6fCMopZ5mHYjenTU9gKwjQErFGEegFpwmzOGQjreSA3DBLSJlLAp8/7EX76zV7cdPP7cfgxRSRRgu5cgEcfux1f+9wjOOvsU3DgYTugp8fDijUDOPfUl2Cli9AxdQgXfm0RHK2qgLsMexywViOMErR1tiHFGoQyqspupHup/RpYNjxfQyHfiiQdUgllKcEeJ1//Egm8DhS1iv7NABtU2jPh3jJ3CN8jsWExe1E0nHLyApRb+3HisVfgO+e/F3mvhI9eXAIdFIpNJFqg8NUZ70BUMis3HW4CHHzAjgolL2fmFJSprhWhEWktYqqyj6jSg7zZA9fl+PfAJzIFOE4G4RgOEu7kpznEcYw7H3wPDn5jP7w4ggsLJbMTzyyN8B/HPYa+Tbvi5WcqeOfbSNoKSBgiSE088Nh7sXDX+/D5zwADG/N44SWmYg9i371n4/FXrsCvb94eTz3eDcvUodg3hDChY9D1AQUOZRETI+lG4pvKBULMEBhVnHDqAojOKAgXiKnYSRz7iqKN0vUexQRD6zpKxgAJUMWDsNsWYumLBk486m6MbDgY/pK5OO6AHIKaD6e8EbE/FQ88dAg6en6MCy+14Pt5PPn004pdZpedZyLpGMJPrwvw9IrFmKBbBAHCKEJLi4Xq+AC8Shmu2QvN3AjLIvJeBzwZQDmfItVaIaGJUCKkZJtJpyNOx1Tcu23SKdSLWq2c7d+pWPoqLENHfSKHUtnC4jfmceaHpuHLlz8BI1wA27BRCWaqqJuzjluBX93dCbtlPj758Qp0z0aaasi1zMJDD61Avf52rO7PYTE8jKUPoZ7ui4d/NwdxvA6d05/G4j12RUTXG1IUtBKSIK/wqFOtBsOMkKY55Z+mHUGEPQM+Fm1XxILtn4ONdgBtEOnKmFoIWUqcbmKkWBzCZSBeCac0AWgE3doTn/7wOG747oGooIprri7ism8Mw7Wp1Gdi/cp2rNnQjbe+YxYOEu7gPIkoGcYjv34TkqQOM/csDjxwX0g6CJLbehNdMKMOuNpU1McIn9ALQ5sB3aA7cQJhnSiCAdwCXSGjKrOR5AZmniiGdINNvv5ZCbxuFLVKblHYv7Ss+UcFzvQKE5FWhyejMJIcjji0BVd//3R8+NSZ+MznX0DY9hQu/OBJ0BJbJU/Uah6KORennvpRDPufwbYESU8c2IaJ0PPhuIQiJb+bqVJzHSeHQk7DSDwGnywrpJ6SOvw66YwIDboOjjZPlciwh7DjDjNw6/0deOe+axChgt/eH+PxJQY29i2EZRfwyOMsNd05FWhuFTlvCkrYD7axBC++AHz89KdhoxennNqO/d/mY4/9P4ClN/p46ekUb9z5WLy85irF9U6gpASbgKQLBlPHmS6u9n18uHZRYWnDZOhhAl1q4OTDgUl/OUGJEgmg5wz4kYmWXJvyu4fpOqxfa+Po/W5F38CecCBYt9rCiuWcOIjFPAaRbjjmHNj5TqxfBxz+5muRM+dinwNDvPvMVohxCh68xcRDd5nYb59jsPSP30e+1IO6P4qc5kCXPgCd8EmBRphobRV0vYSALCaWqAgdj0hKRhv8IIUWt4ITEenI0rSCfJGbj+SCpLKbwER1CEVnN+Vb7ery8dmvz8KaiRC/+IGLOEnhlnS855Sv4u6fH4CCswsGxyfw4G9aYZDNBGUQZilnbqOiNxQ5OlL89NfvwdEHP4sNawPsOXshfnjdLFgYQy1Zg7IxTSHKUeYk8NFcCr0K0yA0AdvWAz0N7J9xWlOuMcK9xlEEixgmAd0yUAYBYw8n+ok+w0mKYXRVQKbiY2fcjV9eMwcRehGiG3fcRZKAAki75aAAyygDZg9qxCzRx/DtG2bh5GNyWLZ0BWZ1LMCP7uhWsAtGUlP0YbrB9K4YkQwhn+NmuwXDKKAyVoRZXIVSniiD7CmaQjyERpdZDb5Ph/ukomZr/rOv14Gi/usiIrqZkbTANgjbU4WeTMHx7+5GWHgE7zv+17js/A/DGuvHeZ/tRqJVkLN0BBXghTtPxtSe53Djo/tCjEcQyVTY1hyFRpkSLtPRIWaMKNJhE/ENeViGjWG69goxjjvDwVtOaYcpZeQLKxDppJci1scT2G2f6XhxpAex70C8OsbGffROa0e15iNP9tw0VGh3xBseMD0UgxaUyucg11LF3c/tAfFCtOQttBc1tJk5/Phu5pNVUBu7CiZaQRC5NO2Cpe2rXEFpWFVWM2kOk2RMZagZuokk8ZEY3Sp2nB0ljR04VifqsSD0fdh5DaY1hJ/eMwN7bn87OswjUWwB7lt6OMh8E3NDr1JAuS1GPR5FopUQRfTDGtCi47BNL3D3i0fBRAtKeQedrWQqMXD19QTaj1DxP4PO9iLWbyJRyfaYPu9ZXPOLfUBsf9smhksF0OYgSkuoVX24jgHyL8RhOwyLFF41GLaL2hgXUSUYVjdGRi14EXDljQfBtGbBdkyk5tOoVRyUWgi7auBLl22Lz3+R0RSj2Latjr3mvw36l3fCsKZjQmzkrQRWkLHAGNZgRtdrV9BSzsGMejF/WjccfwCtOQ2F3ApsM20hRoeqcAvziOikOBz1sBsINKWoFUo0oeyYCSovKeQ6kj+YZisSLFYrAIb+13zAlBbUJ2owzTpsdwwHHOXhC5frOO+c76LonqtCD8/73FRcdPFsjPghYlOD7dBqHlGkCUWzFX7oIajn0DGV7DOtmD2nBNMYR9HRYLvrseN222F8AOhqmYo4NhHUu6Abf8QFF/Vi/wO5ijNA7sSWVnJPToFXKSgEPRKeV8ZMmNZsxGEHSrnXvXr568rn7/j1dSDJZqi4AqNUgD90eWQv4pC1UO/BdRimV1eJCnrexfHvfBNw1Ux8/D8FF158G3JTZuP0s98KRDZGxurktEWxOIZps2KEmAqT4f5JFZZWhJEbB9JWxdlnmzomRobRkk+wZn2KfXe4CYe8w8NVP9gDUhpBCbug5jPGblAl22hCyypEoY25dD7Ez6N3ro5aBHRPoSIhWwxD5HJIpIiS5sOJiLpWU+nOU6cV4SY2kloIk9RLhoeeLsKrxqi1EN96CJbeqZgaFfIR75W0giwxph4ovyOX4Wqzi3G65OMjl6DOpT7TdkooSQ6wCpC4itAcB7fsyo6Jvo2roMsB6O0ivjU3xQC9K4YwDdkoQMxEuSFMvQg9mo7RQWBG7xQYaQpLoXYyQsNATwfDJ32E/s5IDA1H7r5MkbYWi33oaif6ZwVJWFb+dEEJqV5Da1mw/+4XYuO6HrjWYahGo7Byo4jj6WhtJ9wmE7F3wAUfvA+f++T3cP9j52HOjABBmoJKs1TghhmjbWwUXSLmCcpaGwx9EK2dbYA2qCIx3KSIsmXAJooeu1ZaQKKPI4pdjAdDGAt7cejez2FowEGutA4//c0bSJqO1nIBSRjDMEag2xbCSgCDOOdcmdCyFRNCZh3OlgiVK8QyulR4XJQILINIfzGIfholNdgFWuIOcjojWsYA08HIOHcWA8yeTh7JKjq1olo9EMPbMdoz6rgwUNElTPAaHbYxEgFHH/MQXngmhZ1WcOM9B0BLBe30tiQ53HL9alz2ldVIkgi93Qms1FJKOtIq0NI2RJEDtwD89u51OO2En8PR94ZOY0Bnuv1kaN7foYv/6qlNLfZXT/p/+8esimTmYIRFxpShsgwUDnQa12HlInijNmK/DMcuwBKdxg1OO60dX792A0rFCi74QA3X/uB3yof7rrd6qFV3gdu6RIGjJ3GrYtO2nQZruuJA1JAExEb2oPlz4NUJo66jf9NBGFh2BJBsBwfbKmVXysWoDtMP3AUz3hZWNB1IJmBpG+C6L6M6PIyiRd+2B5GN0HWyWEdItAGk8VLo5gBsvRetTg/iyMdEugxSfBF+vQpvxIKZWqhXXFh6gLzdipihIsTwkGcQKz9krDgOuRZnQpCuZ8twbrta2jgck66PMAPdTzI2GfYJ4h5buo586zDauz2gfir2XfQC8pYL+HWYYT8Mo4bUWQ2kLuy4FzoB9L1Q+fDzUyfgaE+i7i1TE188oSH1bAXF6YdjsIwN0KxliMYXKkVd7noaDslTjXUI9ZcV/CeVYF5PYEkRSf8ZGO87HXowH4j74NXXKrjW6nABNoogOxe8xYiGT4S3aWc4GAH0AUX75Q2Vgeo2SKpt9LrDNoYhsglBOB9R0IHKcBme3w7LchCkgC8bEEQxfGKDM6bRWocPv8/DnK6X8MwLC5FqPdhx70fQOmMIlaCC1H4IceEGGHafUu5mPgejjVmstc1uuMh3YCZzYcp2cBiR4xeRY26qRAy5h23QrTYBO0eaMSIbtkNPetAzQ0Nn+RB875J5+N5XQ6TxPBXNUw9eQBDVlWXupxuRGOtg2w4STUeAUXz98yswre05PPXgfpBwPuYtfgLz5xUQ14kk/SIS53J4EV2C2yNX8NHdM464mkJPNBT0FpXZazueWgkkkYuJiamojRGD3UGcDisKuP+3dcf/PaV/HVjUTWFzE5EjlS5qMorQIWjAItAN1kLPb4PIGAVdhpJ4qHg+YqsNpx6/E7xaHhd86BWc+x8/RqkwD5E2B4kV44GnPo2al6Lg0ttMdhQfBlms1aZRKYtNjlzss998rF8bKBB3kTLmLaTFSJCnHPTUx8S4YEqnDa9aUgkM+aIHiToQaxOQsBVuSRAnrrI6C3ovENe544ScpEi0bngyhJzeA+6y56wcQqNdLXVp1dj2BGqjFvKlEjR7U8NtUFSASoYxF8TijAMLjIxQlGJkslEsJaRaYvJErDbVlHWsW/DjmjrPMg1oZoxK5MOvzcBvnjwFh+zxIubP3g5hBJRyeUiUoh5U0eKQECEB6FZJCyqumT5bulbspAi9SJ/3ABy0KxD8ES+EW+yAFkTcTsWC3cj36OLmX12ANPVRqUWYUuqAjJdhlkL4qMIPNOywcxkdnW2oSoRWXbBoVwJ06+ieVsfCnXQkhoGiXob4vSiXNBACAIYPLXXhFgTwNLh5F35MnPEYBasXSY0TRgusvIY4HFTZj0ZSgisu/Brjz9sgPhnbi5g7LY/527oolky4kYM7f34mYm4J06csnXC0VYjqc2A5OXBLIABT3EdU1IQNH4q6Ks1lhCs5KkC69h1F4CBRFbZjoO7pcNwOFWFkWwbGh4FDjpwNP52Nr32xhqnbtiMlRVY4gaIzBUGQR1oP4Fhl1MaH0F72ENXI9ONhSqeDHXYpgTDpcdiLX/z6C7BI2JtLYZtzEEcL0NWdx6JduvCe896Kw460IKO6wpwm+7vhW4gLAzCRoKNHsHjnmbC0blTpaglN5PKZQdQcgZPv/7gENCLCqJ2s5j1oRqlNDQbYsxMXFa5FpuSohOhv5YvLSe5iM1YycyUQiJ3/1Eu5F7iJQXzj7FDmcmi6HZpzBJXnaz43I+g0ZhMyOLn5vIxC6VX3Z5AaMTyar+azmt9f9c7UgGbnISuHQI9JR5RCjFBFYpAOizvWVOYEftc18svlcOU1y3HBWW2ohV0oWGMIoj9gRX1XTLFaMn45ht0ZY2qDEAnpt1wVr131crByFWiMh1W8h2WkSQFJQJwRDXFQhG73w3BGFH1SGnYh9lsVqD03cWwaTgndDxSDofgMmQBBKi+SypIwlrGu/KMyJHN2Kr7amGLaPJH7TL0VptYJkKdQ0TxlGXqGzpTxVwnoT76QMUQxoBBwnkD/NOZSZtBxJyyCn++DHs9SCtiwxqGRqSZpRxqS6DWEbfIZdaTcSFRJR6xHHrq0Kp88H5gamwC9Bk3of89n9FAMndPIH9kCnWzdOlAPY1XnnGkpTAvFUmUxyoBOd7oPLOWPoD3oIYAvPvJet9oj0K0KQixTbCymzIUhnYrDkufqSU51zTQNEabDiPU6Et2G6C7KifK1qD4hWiUj1GXUUNoCXUoQs1/1USYO0ZWk6YypTgkBpdhT9Civ2pVYz9wsVBuGWgwhT6NkhL6UATe7FZsOrXPVnwltIDA8D6k9iMQaQsK+Hs2AEU5R7N/s9V6R3JmMpc4w/y09hEX2+mAAelKD5N6gImBUv+d4Un8kschnWZCqLFxLCQIWIqELxEAuMaAnAhR9xBIgTUqK7Fe3hmBpOcR1biBr0Ap9DVZ3GjzkzmQALFmI6CYjNiOjhSZf/6wEMg2p2C6oxOgeUHu34NYakzf44oBvKuMtD6SCpdJjsCQbhzvU/DXLSlOjmtCY5JvTk6yDKoZsS1mzKgqDp5MSSP1OQjcysVJHNgIwyftHBUVrjHFLijsoU8qKcFbTkWhr1bKYuB1UKJmObypjwnU2tX6j5BwEahLhwMlojjTF/MwJyIROJa1qxPNEEQWQh/DM924PvQp89vw7MFzdiOOP2h1tuQ4VJkciUTUI0XB9aPQRkrEjBqOnDH5HISNO1cjrl1MberrJyYLcgMSCKGQMMqYNw87KRmJbYqxykCtAKU6SaqQTe0TLwIOUcqD/kqKjsjYUTCazDKkwbKsEnYqRv/EcRhto2eBR579WPg0xNd+4qZXdN9MfJLgWUsCQzkln/l4BMAh5RGXDe2sq5pfE1WRm5zkaYTs10jM1Bq04SgbNWYIRBNkNCoCSFSdzQtMyI4/3yUpDnkS2laoC+xrZW9jmKUmGVcHA0BzFgg4TphBsq6EclWLqypJMOCGwjdnvGC2SFTujA4OjaMISgwaCS9M669Y0Qeg/1tmWDO8kVRYncoZokCXFUmMnq0iqEliEBLKkWVHRRlk52HzsYZx0lUGgSqKklx2jklPjqFFBowU6J2CVA0AF6CoxNhWzriWN8pMqrnFfpvYb7YBBmWeQtZzM2B5qNdlgsVdPoJGiiJQ1WHw3dNiUe2NssCXSRBVIlZTlUAaZuobPpI7gLJE9nddRUStlrRpVXTb53z8pgcxMYs9hbJFGIlLiIhAXt6Sy9rL7M4U0GzxsANVsNPHU4G8O2onMwuYg4P04etKcAtUh+7Nu0HrgTnnW0Oq+9BlzM4tU3nqMlLG6qhNxIFDZ0ieXUxacGlSqExOgyMholERDYq6DCH2fVEbZoBZ2bFUGKgmWs/lqKjpezxMy5cUwJ42cfVRJJAVtdLSer8QAACAASURBVHhFD2V6MFMXQa2C959dQq4wgRdffh6Xfuk/IFoNQiVENUhWjs0dk4M2KwyZmhhAJSg2MtGKSqnTCOQ5iqhZMctQriyHA81prEs4qJUiYc0a2qohWiovPoK4EKwKy8wDqu2UQuczaeexfahIsguYlanupaw2nq9asymgP/POyS9bRbFbZCJjWVhfWkxMxrAyI1CdoExDNXYVRyEfR75JglarbUc+omkBNx6nNwY7/QHcLdvqpZmqZqquFsGpNIYSZnXPdJqjSIKVtspOVeS8jEG3FJltQ3JCZPLuLIYenDiyum+uv2p0HaZG6ikbqbqaoEihUqBZp8g1HszM1ozCiisATtSqryiFxWsTJfeUEyxjQpR2zuScPZds83xg1uaKy6XRDooFXNWfMuZuLgvKzV/ixFD+BjTuNza6NROq9OZkq+wOPschiaS6XPURdgHVLJw9m5vqlpIpWXVYDjYt+wrrwZWe6m4cljBhK7oxC6nJM7K2MhSAOQvESS/rm5z0VFUb6+qtR95WTTr58R+QgCZcO5MbT8vSmTOhOwCBh2id0Hqg4lOp11SAGZEnLRvlOuBSjRGzxgZAm4Am0zKNQUs7ZjQD2zFAqpMFmQqIrgaBqAiDugKLN4gGxuW8VCBCIB07U+oE0myQb2aWcdapaL0woUFx3lkPQ+cz0+kZnyER/sHohQYQvZ6BKVE2rx4EDWmp3t/4vFmpN5QiLTYBYp/hYEASJjDcGGlUUxZiEtgwClwt0JprVPtV96P7KFu00EKkxa7O4zn842PUM7NlbqY0tu7eWx/f0rrZoNvynZ/UeGPMRmPAb/61WR6eoFYLvD9HLv82j7HNp7/2Q/PypmbYotaz6zlZcbXAe2lqD4DsAw3AeGKsKPr1LVZx8/5qRdQQWTbO6UprLvsbZ/FhLEDDCm1KRomNp3ClTkXVmEjUuY3zs2uoUKnUGu2p3rh8bHwn4as6g4ZJ45lkiVBqNFvKGwrnhe61rcqkTm7Ir/m8xs/Nsm75uvnGzUNbvf+13xqnNU5pFjk72qjX5jtlaB/ZqZQGIXy3yE6dtrnhXv1MRkg31kzQtn7IZsuf8qC8mgZMlllLN5qSSUOUWz+jKSoe2/zYzWWd/PCPSIA0zMrXuBlBRQihmM80lFIitGwpbi5n2Fg039iKXHrTOvbUuSQJJUQj+fayXl8FDFobtLyYNEH2wqYVwzYOIMyu0mmJ8/60NBNlparrUw4OWmHNPNRmB6N7hvehxUCLZTqQtkFLM6tOU+dH0LgUVZbma7vK1j3rtSJr/LZVT+MEZeWpfIgLHXG7H4bejpSTl8P68prm+rxx/Wa58ScqcpZhq+cq8VGpcknLV2PSUfLheRwcvGvmAtmqOOo46//njlEwf3K8YeBmJh8nWpalIZOmZZrd9a/+r6y+hh3I8m6Rqt1AVePlLHtmLTYnjKbb4rU3/9PjnPCVI36z9b/5e+PirSSYHVFyoAxVr1QrG5ZLgUspq473a65ueAkVM6vfULLKAm70K96cwmu439RkyK+qosr/ll2r/m+URPXD5mHeJzu+RTbN3/7S+5/UaMuJzYZk+NGf+HmbY6Ex8bM9lBHMfkOlzX+ZC4UK+9Wy3vJMGkwK8GlLj9j8fD6eTzEUWW92jShrXGXbbFbbW3WEzddmTObNr1ue1zwy+f73S4DgF2o5pOZkRSHvqk2nzIquItVEZa0xG031HdXytM4ag7VBDisp/X95FaZGx61S6rEO3eZmjyiMZ+X2MNg5siWUMF6Xmxv6iAKPZ+yuHrdn1rQqF32I7DHshexx7LR0qcTKt0jlqaW9yipXA4upw7TIlZ+9IYyGPy779mc6jbow01jNj5RJc5zQomZPD2tklCYoUYIoDFXySkjGcFohmxVzUwHQjcQJzmq4XliZV1vHTZWq/L98xKtGd2NgKP9+0yL/842rlJJa1WS/sw7Nl7qnkiOPZIObA3NzPdWxPyOT5g0aA1gNaKWE6JJiyZuIbSx9w1Whyp/VkefzldWpqVS2uumrPmZyypRc5u7aolmav22eWl51Jb/Q7m22lnKXbZ58VMNl56uyZUosO9BUvFl5eb2aWLZqA+XKUNVouska9dnqtqqeqpK8N39gWZrtxc9bys/nNuXCz82JLCvPlv//pP0akzZ3A7IX77tVm/Ery60UMp+dKeoMMZKWCidAvlhG9s/GtaquKbens+WYqldDAI03dSZXwZvrzA866HtXwQWc1Br9qvGQybf/TRIwM7cHB1xJuRnI2cYXXRAMJk515utzk4xDwm64Ozhgt25wurBdSJJXCpwKmTgI2QCvQ5JsN59KQiMpH1temA5sqIwx0WsQxf7NjbaSCh3TSFBItou40fGVsua1tKgzX5saI42oEKX4uLHC3Xb+QOVJsoCtlNgWGbJ3Zy9aCdmgYX2yHsn/aZPQUUMfYN0fg1ssIJU8PI8bdLZy6duGo1zxhHBr9O1s0KieTWun0aHVwMqsnkyx8myuTjLFruqx+QYsVybbP9kIbRZ6q8Hx2vqpe20+b+sPjQHOAb35MBVJox03H3vNB3U+pbtFlryesuExZXJyYmusHLLy8Fz1a7YRqW7J5zflzmc2n8vVSuOZWYNmX7KmyPpQs1Jq1uSlW2qgcb9CWbYsT3Z/9uYtSnHrFU1zsmw+mwozU2TZtVutFJpl4iZeo35Kcs1+36hTtmfD+1GWjZcqe3asqXh5j6yfNWXAk7aUo3npFmHwPNaJoFH83LhOGQbZ2aqItBMoDv7cOMCrmq7CrLjZj0ombM+G+NT+DRV5sxkbx9W4bd6UFVAuwDibP5uKnvtHakWydX9iubaqk5LDlppNfvrHJWDSV5wp3TI0I1smUr7cEGGDUYEqiEgVBpe1ZObEYDQDKalWK5XGzRdGLqgdfHYxukQY3sdoDK2cKVeWMw2QCCnpdUVxZVgEGOoG0KKsVYXbSHB/5V5xG35VdWGjh7GnUWHzr2FN8HNzAtC4ScIa0OpnimwzvO+1Qmp0fKVUm1ZH8xz+xoGnAaGJvN0FkQqqtVGUim0q7CwmUp5RAZLObCNSlaF5PTtrw2/dtLA2D47sgxpTPJ2P4eM5AXFMpNx4yzq7OpPKqTmyeD4HDsvcPNa8kTrOyanxoGZR1Eilr5y+2cYmZeM3RvlssdSaF7zmXfmd6fNsrhYaMm/gfKvJSG2YsSCZHKmQMjXdVB68ZqsBzMHcKLeaKPlIpQ+4XOdqKSsDo0iy6bJ5n2zjVP3eWPWwRqxxpk8akyEvpxxUldl7+Wwzm1Sb1cuE+2rFQswM3k+59Rr9jGGJSsmyUOwX/NvSPuq5DRmzT2+WfqMOzXpu+aFZAL5vuVf2OSt2pih5A4FG/zrvr1ZtjWv5E8tE65n9jm5Jtf+TPZ3lZ/03l6VZbh7YbGE3nq8ew/OzFmOJVBfa7LLburzNSrFkNNaya2hwbHlWo62ap77qh63vNfn575GAliQbJItY0IC4BcLNFJ1M3A5CbT1anT7U4jIscwqi2AJpOompXLaBogxkoUlYgVibiZFqDlPyBSRVHXF5OYyIAf1laKaOVLlIRpAmLozYzTaeIhPIhYARAlGBaXDwmUarWXCMDHVcC6mQHYiVQ5x6sBjLSouJHZi8fVKAngoMZnuYjNkeRpS0wKLVzdWeCjAYzUCHuEOvxli2ocTY0qZF9ZeFxq5LRZDFXfOdr+bSVVfRIurQ5H+TEpiUwKQE/rdIQEc0FVGtA7FfQEBwHp2RFg4+ec79mJu7Fz+6cgYccz4MKWNisIB5Lfdj+1KEMw6jVZxHXXOQarviv07qwg4lDU8+ouNtb/kBFk65A77fi1Bbh0iGUfOqiEIme1gI9JVI7aeQ6qOIQmJIpASVQL0aKAhQXVuBOBhT2AsEVfBNRmnHiHVBLW5BqrUgMdpRI1OLJAjqglQfV+ZpLK5ahscMZqGSpqXGmGJlcVOG6eZlcVPZ/j2S5TX/yHV/zzMmz52UwKQEJiWwtQT0RGhXpjBzjJpI4PtMIQZmzWMG3q448/3LsfT5Mfh+FU4+RMUj+M8QRM+pJJfxiRpGvRAbx55Ha1cVhfYQExMGAt+GaSdIDVOl5OYLDL43YdgmckRg8x3orgXDSZHGDAXUkfhMcEhg63lYVg+SpAIJXeTEhRbVlJfcjBzosaXolnLkLayREkpXEJyJRBCC3zM4mWRyEbExYrXBKI1Nma0r31zCvvrYX/7WVNBNZd38/pevmPxlUgKTEpiUwD8vAdNyNEjAkLoazNwIojgE/5310R3xwuMhrr9Vgzg16G6C+36pY0r7fJR7+9DXH+HlNS4WzO7EuefcjHvuvx6/vO2b2G5RDwrWdOTc5xB5Bh59oAO6niLRn8asuW2YM7sL9dBBPrcjnnxiGNXRCYT6Wuz3tu1Q7GToj4vBTS34/aNLsfPu82DWXTzzxxHsdZANp2McRq6I392zGqU24lm2Y9e9ehXJbByMIu/MxIqVA9i4dh0Wb7cQba2cTBiBEW9lUf9zQmsq5+b7P3e3yasnJTApgUkJ/G0J6NwOoN+VlPeEJ8yZbfCjGiwzwuDos7CNddBtF0Oxg5NPeBxdU2zc+qvFePAPt+BTn3wOCH24eYK/OHCdAIEHTEg/fBHc+LMER779Chxx0I9xxNuuwdmn3oqVq0ehO4IlSz0cfuAdOOLgH+GwA6/Hfb9qAZn0GE7/28eW4sjjvopLvlLB+079I9510tfx2NNr1e/X3zSAAw7/Kt6wx2dx6Lu+giVPALm8jZzTiSgWXPjp83DAWw/FyuUbgChzUzQt4L8tjr98RvMezfe/fObkL5MSmJTApAT+tRIw44goaQwlyyH1SyoT1yH/mlRxxCk5LH3hJVx7xXP4+BePQNl9E/S0gvGxVXC0UbT0RFi12sIDT4xj97e8He0zQ0g+gKcvQG30WJz9/hdx1n8ehMAzMbzhSNxxzwO44Se/xcEHHY8zTroJu+y9F3baYQiBdybeeeivce2vp+DIt78BaVqGmZ6CH363DbtOG8d737cHZvZOwW/uXomzTliL95/7EeRyPtavHMVxx/4U1/z0SOy9d4/CajjmqENR0Kagp3eKAuhhWKEI8USaURh/rwC3jlb4e6+dPH9SApMSmJTAPy8B3dILME0HhjmqEk9CqcCxUuipjVNO3x47v7GA71/6a4wOrEBiDyA0THgBswYDuLlx3HdPiN8/VMYZHzwNC7bZBSkcpDIAM/8IPvWFFlx6xW749jU74Z2nERyogmkzZuBnP6pg+fKdsHDPdszabiG6ZrcjMTtx/kfuypDAiCyGtdjjzT5uuG1HXH7VYVi0TSc+d+5tKFrbobd3NmbNXoBtZuyDVWt3wXe+/TJCZg4mgmPfeQyuu/4rmDa7dXPoEkOSmqnMrxZZFtHx6mOT3yYlMCmBSQn83yUBk1hMzB9gmJqpaO83IEEeUX0bFErAuk1LYMuOMGqzYWAMGza4CBMLMSrobI3Q1u4Cyb4I6jEMQiNGgJVshJX24cIL3gIvJratgST1kJAIM6nCyJlwtO3xrc+sB4zfQU+OhIHdUOmrIKfXkCfLCVZhv4PXYvYb2zBRT+DYDozCtqhEU/HpT1CIg7BNZmEtQG2C2ZPcGsxiYMO0rj6btgVmfYuqYCN2V8W8bpWc8A+2x2vD9P7B20xeNimBSQlMSuBvSsDUCequYtRJqETYoFaV7OKWqmBEyDe+8U6871gPHzrLQM5dgSu+b2DRrCLOvfAU/PQn7bi9QEim25CfmA3N2BZxZKBkt0Iv6EiGU2itnqJ14saibo7A0tphJC50EXzlu9MxY+axSIZ9dHQDdml/VZaAVFIooRAPKNJRxzbBcGUt8lDIPYsrvr8vHIdA+mV0T9XR0k6FHSC1iAtC5UwkPkJn+hCNkR/0xHNTMQP1y5AAGbeXJTP8VSnR6FZJATyL2ZpMKmH8dfYD3Soq0UFleDWyKJtB/nz/Bz0nDFlkufWYCTC8T5ZpqRIjVCw3Jx7OsE2sFq+Bs8KTiYPSgFplGZqECSrl9zXfm4kUKqGFKWiNwrPO/GhWM0zxpJEUxNur7FDKz1DsKLAZ2072nCLSlDjOlBV5Gk1YSvahwqTOAtspTxuayrizVfikxsxUhk+mrkqs0VWiEmMrSSVW3wq3mn0pUElUCqMmJdEuoUeJ7sgCM4kqm5BZBtVG/N5sQ96XZVNogqyHh5RodNwZER2Jwo7WoJtEkWTySw46YUCJH0NUScqVmbsJMW6YEEMrh241Ppvt03wQ35uvvEq0aYTfZwk9CviJuzG8Z0t2IsVJvJrmpbR4VPmeh4lZgExpPIPJAWxb5hK8Og+m+UT13mh3ibPyicGkMFZeNQ6EmNqUm2KXz9r61ZmwWT9IKGOVyESESF6e9RESb6QkGeAxnqqgiAnv0Eja4nN4vJn5/qrCTX75eyXA1MLGNRmIS9art9zmTW+eggVzXfwv9t4D7rK7rvN/n3r7U6bXdEhCElIYkknvmISIiAak7EpTcUVF/S+LykJEsLECK7sGxFUIRMGEFpGE3oUACaTXySTTMzNPv+3U374+33NvZoAU1hV32T83ufM8zy3n/M7v/H7f/v18Pvu5IU85IuLCy6aMmunCi87gHW8WLedd/PKvnMhzXrCcJFugUV9Pd2nA7F4RdZaEdnczJpc7knwH+bAk76v5dIaPXvc53v+Pp7G+dRi//ht/SpIvcv6Zb6TIfSIDVhdW8CJZXjNOwsWl/cwnX+Rzn/f5r39zhoVHfu7Zr+CKK17Akce8gnrUJC/61KIIPypJkwZxTSV76lirAGxM6I066May6MDVPsZvNj1uFEYZtZsLCtV2nuDbKqQ4HUsvVdtAi1crVsd79JXHOPjjv/Q9VSUmB0YLfzR2+6Ze12WZVBIcaXUvDaVP3oVOLQFgkqraN/rewX9bDbsdrGqDt07BUgD4ugZ1GEpyHGgXNuEyrqKxNniRAFRt1sItHs+DNRLZ9Vf4IFIeNk47rCBmrC+7qknX921jq6tU86YvSrHGeKMmJb3kNNcamLoX9X0J8lDCTt8RnKg69ATvqnelnIX7or+rNnOtbcPltnHpNLWqwVXXWvqEYyUlyFpfcAT6nrojhZ8tJSwFKJRH3VXNdUw5UnTV/RqBlo1uazUKKTCNRp8fKVcTmNX9CkyJSuibZqkUr31fjV7VlVR7ciyVda0iiND49T0JUl2Ajq0x6jyj+VPbqzorbT6Uo6nWhw5v3ciGMz2aDH3PFP9o8PqhVoRCmKqjw9ovxhxh8yGckELnty5IHUerRd2Suj/qVA5G81Qd4if//stnIKwAzyWAZDloU1a7W/XMChBErkGRPUSLLrn/OdL8qdTCFlk+JGcWLTSV5E1ONckHTZb60Gk2ySdqhJEjFPi/lzJYSI1ctDfX4jfeWPCt2z/ETV84hJeefigxt/C123fwlTt/F09k9H6L3KWUS0+lLGIacWaWzAc//pu88Ge+yrXvgy1f9xkkBZ31mzj3rAtpRM4opd76+gf55Mdv5l3vfyEnnFqQpgI+FxC6CEtljWj7jJDFbJPpep/gYQwYBxZz9VvV2mwWr9a/mTbakBliDLGNNdrAPiuf4OBP/paL+gcEq/X2akNqzKONb2h1snalPLQptaFlaWvbVJ2X1ZgU4qo23cF/qwVZG/1RiEsBahlUbHUdsnwtdGRrQ6etNmLV522AxaPuVClDYblUIzMhpcky2aDBSAhrfNrP47FLiGsCKwtVr0qAVDRpErxDQ2QU2JeEt/PmR1Zop/IwwsTIaCUYdLVVS7UdZaRYJexncSJg0EMAYILQFdGuvAH9bQhymr5ImAjV58yarfg1SwMC08ua35EwDXX/dWzF1WQyjq9HnxtjAtgJ8fS3Fp/JMe2zA/fK2vfFlGOVVyPBPF6OBpGg5mzxZy4zpUQoKztDTDOlGtMM20YY2ZrDoMJwt7Z7NXjps84Mlsryl2TXuYXlU7WMo88g9Ep5lhUWvCmMsakuL8fwpseXWMFCCDveXFw7Z8WvWSmlkQKw9nMdW88R5G01sz/59184A6MmfS2kasPoJkqQaUHJKZRrkyZbqTEkKksaivuKImAwR1PWjNvH3N4Ze63RnqdRg35/luFwljyvkafani0mOi1ydtBfmmP9Go/rb/g1zjvlcG6/fSf33L7IzXe9k0MO02YTi8UOmmGT7nC78cX1uxXQ+/HHT/K+D1/Mmsn13HffHhrxaj75qT/l6Ke2yNhpbCoPP5Dz4P19vLJldFFBUDGe+J5YVMZgNbY3RkS3P8TMaYM++pQJq0WthGqtEgi2aYTprWe1gazs0XbnD3H8J/qIuevaGBWug1xWcz+1of1KtcoQlD2sUIOAswrhdZvIlvKVNfz4T1muEjo6rslPeR7WWiTiMF2FLFIxx0hQSjhJ4gjytrIShZKr1wzvQYwpxtYjwSkhrI2rtaVldmDOxvgilastgajPH4ADqUD59XkJQAkHCTFt+r5hjVfTpXnQezYR9tHSsEyqkkzZznpWQqliwKnsPSlTXa8os1JKTyEaXZvo2PLqaUaLokBqBavGVo5wR2xI9n0JQh2nGqau3+bJ5ktQr+O/tYukxOThVOuvWocj4a4wjO23cSmpPqM9OFJsbpWBgYkVS6GRau51TilYacURm4p5Dnpd0LsVG7n9HooMWKEnhe20jg6E75zvKEZY8XZvLZejKdV8lRWUr82JKHSkIMrRfOlnaQrcl3I2BVz9lKIVrIJdwxi05YnW90/e+6FmwCsViNbDjIlqIdnGVmzOFQSFY26hZfXNUT1nsr1EWuRkQcT23VOsnILYQbP5EElxDy45hHrwNLI+tKe1yB+yaOWwv55eukS9HVLzV1DISBabc/gwbv962tMhqXNMRR5JXrCvl7C6ow5F8RdsoeyvMyyPMoa57ijWXMLKSciKWYJwPwFT9LsNBt2C6akpwlpSGTNFrbo8sxQlpCvLTrx1nuJ9T/AYGUKVN/4YnzMjV6/bopR41K+VgNDMjvCVHuObT/6SLHYTdBqyDeQHv1No45lAquBLH/2EWZnlCE1QAk7jUh5Cm/nA32LBroSoJG4VbzeredR2H/gKN2gtSKCOwxgaT5WQ1YYVr6SuWBZaJb4VypCg1QSIwEGLqwJUsnGY2S2hL3Gusei8YklRXDowEmAbqPnekvNaRwWFmIJMqAno37PuUxdIKEoQVdZ5JeQ9E7JSAIGUqW6LyUVZ1NIhI4RIb0jmRQSu8rhGO8E+KgFc2cZy9StAJyPHkfKxi9UhJax0zVJEY0u5OoXWg8StLsE4Oc26rPZXdb8qdMlAeO12r3QeHVjH0VNjrq5bseCyqObHjGez5qWalaTX+WU0SBFIcYgSb1ApZ+U4WGf3TtNVeTzjtV/dHs+IH6QUlDeoHjYPGrrTnMpIGb1hClmhRF1btafsVo6+ODIbRh+WZ6egUzXXoxd/8uNfOAMVHvWjd6I6ihaAbCSL6wYltVpJLZB7HBIyTe7twCt8NqyB4WJKvR2Tll1rEe80N5Amu4kaHYphGy+uk6Zi6m7hqwvSW2LQn2Wq3aDX7xJ7TSZXhgyLjLqksmA/oiErlss+3MkjM3O0anXasUIwkBQpnWZA5Ack+RJJ0qdVX8YwHxjD9kS7wUTbMRR5TF7DCwcUZWKLOjCLYbSqtHAVc3ySx9gosHjgyE49wHVXWWLaeJVV4Run3/iQ44U//vt/56cpBNs82jWVpawd5Imdx8TkKO5rv483lxJp2sAjyWICU4LmwN+i0bLxjxHvtBlLQcWOhIUpicrlrkSXBHBlPWlj+l7XOBklrMxqM9te3pkEjQ14JCW1nkYzYGGh8XXoGvRQdFPxTlljY3dZYl/Xp59jq1qST8m0MZGsBIyuXYStlaWvo1l82dbwaBh2HbrpY6mj122Qo/xpTikqOvuvObKIq/FarlAj1EAt/iolo25X3XeNX9dbzenokCPhprGOL7qKJ1eToPtTWZ06g5E52LGr8MSj8zRSYGLOkcK2keswxn1Ynde+NhaxMkAOfNni8/IEKubbSls9Ohyd1/aADBXN20GPSr+YRqrm/aC1oPU10lRSgDY3No0S6pX3YR6UhVLsBAcd+Ce//ktnwCtcWt07S7iNF5UWT5Xd9Tyh2HXIvTmyJKUWtPGjhIyAQR5QF42WgyQb0m54DJYCGp1H8FhGOlQyT+BNdfICMr8gNOzmLiVzxOUKdi8ssnZilcUau8mQjshD45yFYhtxEVGviWS3TbJQEvmTlPXCWKkz7V83pMwbGKWbXxCEOVneNULR0Jug201pt4XcVyWaFG81I+dRCMeRwHqS2ZP1WHkZ1QZVBmocA9QmrawRJZpGQs8sHtljWrgVkeqTnKKyUg7aZAc+L4t5/KiOaRakrEiz0ior2YS1xSArIVDFIysoWV/MOxI0RWXdHPy32vsrxSN2narSRJu7Im/VT4VzRG2mMIPmXJaUEoNaK7IoZ6yj1SjW6FuOwdM1S5hKIJspVwViLJYvWTeWfJohw86qiGFLFkwAB25yZPUmlOEiOPFNKkwxY5acX66tBLO/H1esqaoXzP6VMBtVTpgESxH5q0oPKmtPlqaEp9Ab9QGtY3kDEkQ9SkRgoXTjlN23KqAjizUy58DYjHx5AFWllOiojJRYysUSjVJidgmPVoJIGciyL51CEkqyjdfLqHJDQs6Xt1LFqqtYvgyIkeBXgnakoC20ozLUUGNIqGD/VYlSN5TfSkcvVaEPE966Twobyvq2Kx+VYejYVVhnTPwwfre6ydomlSyo7rvWlAR0FWAzxamQjWjyTKjLehZcsuZa72qdVXMx1iHj4/7k579sBuRzmjatJlk3sHp6VlblyDMRXnqEcYOwvlBl3osGYRAhAlFXpHhxRjOEQdql2V5h2NJpv0HUFPB/QOYSY9qOvICsKImCNqFIBshYNb0aJEm3GAAAIABJREFU140oG0u06x0YSNhupxGtJfKXGKYRjaBDrb4Epaw3CcSC0MUEQcNyI/3BAmUW0YoSoiilUDWAt0ijMY1zap6pLDVtUHODxz6sLb4nmzgJeVkvB+KaKgXUppMCCP11JqDF7TiWP6o8MMvNkPae7PhP8r7FJcefUZGO7ldVeaA9Ho5iqJXgkfus+dHGqqw86VtZu3qMx3fw3/KMzJpSLNySobrOaiPLuq0EjBKaEg7V9q1CBPqMOBEl3HX0cRxYRt2Ys6+yOKvRayBSXBLaYwtNn6vyDxKkVaxa1nvDEntO0LWFGpcUHy0oy7aRGFdkD5I1qrcfHVNKwa5bcyALsbpv1THl7kvw6Ly6BnGC6tcAAScIv7lwAwIzhxX7VbVChc1ejitSpKMsAGhXata0gT7K8FCIYCShpcBMxI0qZ0qkHCSozbKws+s04+oTKceKKUNCTgJPgl5zOxKUKmUMVHop3PaxUtYnPSPn8E1x6OwK8VQK0W60vB4X46tqQ4dTfFw3QoQe1aGr0rox3rbdw9H3Lb5SWcOesOFH68di3GaEiA2+Gq4YoGxelC+w+2sXUFXJ6ETjW10tgp/8+y+cAa8sF11V13owwH4V91T2XS4lwe7KihDAf9muAPl9ZczrON0gA/ofZ9ZlgVWLXdaBE0GuJct1HG0icSUGeHkdCq2gLp4dUwniGbJymyUjkjwiyo8xeaOiD9fYSSJ3s2zRUAxkuBJqoSnu3NuKxz68cg2+O8SS2YWBTKk+d27E2yjLVgu9Yg2vpNOj++Fxp68SAzPgFKZZTpk0yMuAuA5phlnzaVZSiySUIEsdArrKC3FGpgRM27HHsebvP9EYvF9Wu1xHa3eXkPUgK+cgjYjCNr1hSautiVRyF+LaIsNeTq1W4IURWSHRIhRChYpzvPZW0mI1kTfBoNhiQqwZHU6eFXjxVgKaFIN15Lnq1++kTNbaPPnxI+T0YLCeKFjGsO9wClmVBc1gP0X0TYrgULz+04nCnKQQQm0VwxX6IlGXfDBJvZYZEcWwnCFWlUUWEgQhZVEpuNKrW/6gSEBFGYIS7/Uc9bpHms0TCp2x7OK8DkVYo5vqOkuaATT90kpBI38lQTBLOlyOi/cSeRHlwrQpY1XYlbEGt2AxWy8XVVyXKEwpBpM2nrARmQzKMukFkbwGlr6Q92fk30FK4fskaUEt8igKxfO1T1KKofoNQOxsWdFDSWspnSAQ/M0iQekh0uZUsOlBThCUFIOEQMZIqRBeMQoFfpdBuYzAX2V2eiSBmzoa8QJJPk1Y+nhFTq2e4eYbpFlBbSIgCx4mD+dosAoGIuZo4/KCshYgSPeozCpvoqbmtJwgUoI9gKUJCHvgt+x8Im1Os/3E4Qpc7kgHHhlLRLU2tYaHKwYwlCfax2t1GeYJ9bBDmUgJiBNVBpo+37F9njNHI67j0hqe8iehvMwnekgGPP6j8oQe//2x0fH4n/h/453gyit/50oTIpbtH6la82dU+SHtqBjXiGDWiG8VB5N1pM/K9anYtc2lkxK2pI7Us4RKVa5lv+rz9t7IcjBXVVq+CZFYyvfjBTPgTZAlk4RRiO83cKHsR8VZm6SFjxfsx+UpQW0lAyQIc1SUn6YBtWgVFLJA5B7G5MPYrAY7hyUNq1Imu0r9M7rcJ7qVlfWTmTBCiw8dsyRJ56nJe008IjVlaOzFXgu/aHf6eQtfC3yUS3k8QV3ZX8r4+5RK0FnJU2XNpPm8YasE4SSxxZUThksFtYZPf36ORmOKpNfAjypXWbyOCgN5cUDpLeH5bYO5imiTDFK8YgLfnyMIVoISrNZhukAUxqRpQhBCka2zDH8UTVFmIaWaWfIajdYiS/2EevwU/GRFFQP2UwI3awLYD+Rd+QwGdZpTCxAWJuAiHVOED7U6CwspYRQTNVRL0SWXd9XpEkazJElGIGXjhxSF7OQZovpK4jQSVDm1OKcdhsSycLshodfCLXj4tZrZv76/QJ6roWoC3y3g1xM8/xHCvE0wnIREXqHi3IsWHgmjFkk+NGESKnQRL0AQkQ19K+MUFVx/0RH6s0ThPgJZ9ZlP6Mf0+gXO94lbcxTdJqHVcifkqSP0fAJZwE5NMz5BGOP7uSXI/UaL+e4SjUaNoPQJcnkjywhdi0agQracWtqg5hKyIqEWt2EeahN9+vmDFEGNaFIGUoGXF8Qq/VRJYRSymMwRyq21WH1K2JQRJYhhH3UheIX2XWrk4UQK/QwJCEm8RWpxg36ynyjoEDb2Gz2ekoy+38UrAoZFn7KhapGCWKEwWzsTZLLSc4+4NcD3dhF480ReTtL1CUPt63HMfLzZHuvnE+0+vfdY3znwmn77/8PDBHXl2owm1dy/UaZB8tZkrtw3uZqyBhS31ILXmpArbj3ao6SJfcqCdGMhLyvLMscmxCuXtIrVqZQrJU/r+LUBBH2zVHxW48ppKzX70hdu523v/mtOOHEdrfo0QaTi/oQs7TA/n/LmP30fs7uXOO744wjDjlU1FE6VHhU1l9Zm4EuIyX0buXCVDBzdW9nLT6zRTSelMmPrJrBMvzAkjFuQxxSx6nS7FGKeUZOETLmghmLoiiHa6bXcTLH94JL63sy6lGNlmYuuTA1EcbOH82rkRZ1+NqReb5KXEtaTltPJpEPFkON5ZANnXZmhF5klmFMQ5h3yzKMeLSOop3hZhyJLyQZN/NBV80+buDlBpoTssEm9PsF/fcsDfOJjD3LmZR1qCuvoPtZW4FOnHEQMuj6u0Seq9yGcoZvNEzdC8qRGEOXk5SxED+OKHn5YUMgib03SG/r0+yGtVh0XzJG5ewm9GN+bJKrXKBgQ1mYIw0WKrEau2Ge9zhvfeAM3XD/L52+8m7R8iKccs4KsjAjjgKQcQDSDM6s2Zlik9PJZ4kYMxYBSlRVBg0GaE8vC9Hwyhe3iIZFrq6yI3TuXuPL3rybtrebY45eB3ze+0JACPxxSpMqvbMDLI2qxOioLIieloGq2qpROjN1lMbRQoWLX+Kr/Ti1JOhiqZDKg1epReHsroe609nX/6rz2dz7P176c8slP3MHMwh5OOGU9g3QRLx8QNWbMqx0US9TCDknmqDU65N0O+TCiCFOi+hylBGVREiiH5Hr0nHyEjEJKqhGSJgVhPWWQiQt1FpUGEE6QZhG9pSbNiRnzBL14D1GssFJGni5Raysc1bEgaZH4BNkKs99U2kdzCd+fJWEnrlwgiGqkZZ0ibKJtE/1vStIDIvmxRfYP7iiJn5EceoJ991jf+7/5tbAKIo2y9CaO5dBVFrPs3YpxeBw3qxK+VpKkuK0vC1OxPEm/8eRoaqsSILtwv2sdYFXIoXqvagJQfZ4jbIvXUEwtHbJ8rtILYUnkWnztCz3e9efLuO8rjg98NGJqDWS9w5loZezYkvPnf/QVLjt3wPN+/lnUGhFpOiSUheErXjdL1EwgXf5ollrDHI/S/M8nEdIav1P+UJaBHyhqY9Z6GZRkeY+skHXdNs7EwinqqORZQEPVK5H47hRCOmguHmMlSIALTEoVKWZdq2RNACxeThw1jMJMSdFmDRpNn1xWoFeSy9pNAxrtAZmrQi7tKeGuQJ5AJmutvoSLE1zQsPrq4cCZkeP7TUovoYxzGo2cwdDhJYoFtwjqi7Yj3vmWPt3hOl7/X1ZCfYHhYJKwuWi4K5OdAPFADIohWblAmkzQaqw2oR/XS/pLMa3pQ0kU/+2tpBQvZpySBAPidot8CLOL+5iayCiKVaTZWlzapiwKmh3o9ybx/fW4+u0k3oDf++VD+Jv3LCPlamCOux44imc950zywKfodvEagglYRuBNMshnLF470dnIMI2JwoA0XyLyE5qNmGGaUY8bDJJZGuEEvTSl2ezz0Pa9vOeqh9iz4yae+8K1ULRxrk0WP4LvDiWsCeVxD75bwWBRlVCxGidxtQGpKfHIFFKpq9a+iBTmSUhdaJ5DvaEQlmOpv0DgNWk2pkldwbAX8/Y/28lVbxPMb4AX3MLRnw547ktOo8gnqU8n7N3v6HSOoqHEq1tpyk8rK61BWO7FL7TefWqhwmx1yqSPF00QuRo1fz9Zc4G+kq4NhUH6NGtTVqvSS5vUZKsUOctXap0uJ0tWkLGDeq1nCd2w1WQwzKkFOX7YZj4PrOu8puhcsRdXxCRFh9B/CmmeUuZt8/S6wyGhjKMntoMeY0f8r74keaXHj/xEo/P8n/kRQmvUwKGovwS0KhwkkSTS5JK3qpHpb8UgrV5Tlq3er0p7TOg9KqhHGWJ9ywTjogkAlU5ZokvZBSvdqd6Xi5hlqo9t4QdtCjISi4M18Yrl4F7Kt74xwXMu/iTXfO5pHLGqCVmNZDCkxbnU4mlE6JKXCaF1MMqqia2mNC/nUfWHWfCq57WSs/ENHf984om36jU1Efg5eRESRA0LrZunwIDIKgTqo0qBar30egWtZkA6ECvNEwvqaoHJLT2Qdamq52pkEuCuw7QKa6TjiqaF/FR94IUJRa6dKttwwmKrYnMfDHs027KSZKp59DTGoEFaZrjmHjKmDV+iDH32pfewzH+qxU21X1W70OuJDm2CdWtPYes2ea8JQzdJ0IS0CGg1JpgbQBxlxH5qAlCx3oQ9BPFyYtch9eZIWM6QjUxPpHh+3YC+1LShxLILF2nVVYynJpMJ0nKRVkuxa1UODKi3VpPmHgNvXhFbPvSekHWTm/mbL61mMPsIE/4qIq9O1N5h7nzhUvzyGPJgiVazSVko1dagFvdIey3qtQ5ZqYqRaerRNGXZpeYr/KO8yG6GPMxTn1nnfZ/4bQ4/VGQTu8jzDRQ1ze1ykmFIWZ/Aq7XoDwLqKyqF32WBGpMWMhJ0QZr41BsT1hSSl5k1DCXFgCg2d5JBntNsHmrJ3SUVU4kcuR3w7rffzLLgIv7qI01q0XoK5kj6dWjux7kVNNrTxKp+GrUu1VryCByp38ev1ynzhKKcpfTk6U2Txx0zjiKFa9x+BpkjjlUpo9qlSZyS6t4h5u0FgjGJrOWHhAXCtiOkaYpdsWqti7ge4IoWSalQkxqhYDGdpdNUSHAnBMdZojeo+RaJ0VKte4tEJh+eeH/96717QGDL+DnYqv7XO8f/uSP5VZfd2KIeXaAJXVl1Wg4FpTW/VO9Zm7RTaVBmll9ZqPxNDSjCH9BTlQBVQ4posKolXVnplTWrxgwlGFSkL77EOmE8wEV7KYM5vCC24v5Mxmm4D/wv4XlL3HGHY/aRw0iJyLwupWJl9Ki1VCImIaIOO4ezLLiM6ElC1lXKRQrGOtu0xDSmH/4hAdnvK+FSJ2rEJG6W3A/Y+8gUz3v2B7jwgq2cd84+zj8DztoEF52zwPkX38gnv9Jl0BwpuSc5XSAvQA0WGt6jj4id2/psPucjvO3dD/FPn4ezzriNi8/ey9mbv8tXb/ZI2gMWFmIuOu93+Y1X/Xf6WUiWdyxOfO0Hljj/tA/yrW94vOG3Si47dSszs4fx0xd/huee6fjVKyLi+BS+8ukOz9o0y2Wnb+X6jw9ptxpkzNAvZ+m5PjP7ajznvFu45PSt/MWVdQQP0bNYpyzEDi9/2QrOfuYkl541TW4bPKHtreZb/+Tx/FPu4roP7eVv/qLLJae/h3/+zC6afkRMk4jVJIM2rTLmodvWcfamB7jk3FVsOjnkxs82iMJ5mvPn8MoXy3lfyb6lnDf/9gq+8ZkzOPXco1QcRD+DhaRN4m3kFa9Y5MyTc+7e0+Hyy3Zz7qacX391XTkzZtOUfhhz1V8VnHNql/PODLjoWXD2xQ8yMz9NxAnsfPjp/Ne3DvjCZ0KK7DD+xzu7nH/aHu65NeX1v7qTnznjJrY8PGA2gbPOvJazT4HLzm9x0eZb+Nu/eRgXNMlI6Q66pG6ezC2QFQGdWp2td05y0Wm38eyzQs7d/B0+8vGHiesFE/EUr3uZGrTOou8v8Y53bOHaT32Ucy49iqip3bKcInuERj3ktb++mwueAeec4HP5OUNefNlX6ERNZgfLmUkcQXwsH3jvGk7dNMMl57a55OydbNkW0S2WE3vHEtDljm+3uPSMlAueWXD+0+BL/2RwKlYR8osvegevvNxn686ASzYt8rqXN/FZRo0V/P17W1xw1h0866yvsPVe5QcVilGf6Ea27DiVC875OpdemPFTF8AZp+/k/ddmlPVpEiUtf+QPyZbx88DJJKz1/H/m4VzhXOmcy5396srUObfkSrfgVBFSZLkrylmXFzMuTwuXpUOXl4/YU+/liXNFnrqi6FVPfT5zrsjK6nW3xxVuwRVOx3H2LMvclUXi9OUsK5xz+13qtrleut0lRe4y51zinPvDN33GRVzv3vkXqbt480Nuw4ar3cNLzg2dc7fd7pwf/KJ7zqXvts/q82k55wq3w2VJ7lyWO6dLyYZ2nuq6Mlfqev9XH7lzSc/ZPC30drnEZW7HI8416lc6uMeF3qyLBAdoVcF3O+K3unde8y035+ZcWZb2fLxTlpp7u+LMVb87Z/OTO/fPX9zh8D/uWtNfcJ2pm10c3uoi/2bn80V3yOFXu89/O3GLg3nXaVzhTnnGq92S2++Grmfz8/rX3+VC/rv7wIfvcD9z4YxbE+9y61df52I+5CZYchPMuONO/Ad31IZvujpbHDzsNqz8mvv0F25zQ7fgTtyw6NrxLnfshi+5Bje5tvjea1e7P7jqrW7RLbieK9zv/OoDrsZe1/RVq7LNnfe857thut/W0rV/nbg2X3dPWznrnjJxj2vzZvep6x52RdK3daFrTQrn7rr9Qbdu6s/dRLDLqepXBYHTyz/gbvzit+w4y5Z/xsX+nOs09V7fnX5K13W1Qouu62UDt+ic2+dKd8opXdeo73frD93m4F7VIzmPfe4//Pbn3D43dO/4yJ84mq9xAXOu5jnnh3NOXERnn/cht5g799FPLzqPB9yLXrLT7sPv/+Y+B7e749dtc6vD77qY97k7H3TuKUf/pasHN7qIvUJAdy2+4TqNP3DXfvQOt9Dru8ItuqR8yPXzbW6Q99w9W+bdEeve55psd1LzPre5teve5j79+XtdUTh3/IrcLSd3TS91XvB1d8zmP7RrWnR9pyvN3Kx7/etudlPc7zokrk7qmjzsfK5xL/6Fa+xeLxSJe/f773JTnS+6OvtcDVE8J+6ojTe63d3trpuX7r7te9zhG69xPo+oKt21Kd3qyavdp7/6NTdwS+6UZ77JxdzsTj5EQaQZd9pTv+nSwrm/e3/XbajtdR22uIivukNWvdfddv9eG+POvnPHbvoHB192UbDXxX7P1byBm17+d+66z3zX9WwDar89/nO8Px7vpytz90TP0iWuemqzj8/zeLvtx/d1vwJOGYV4TAEpdKFuLz1r+COsDLUS+4Gy2DVLngg7Q++pHMk+4wvpLhj9rte86neFNAzSUomyKpxtkGUWZqlsbMFJhqwljlZZNa3qns1NHjYIuYcVUw9y7RcOpb3yAc7Y9Hp2L2T0w32UHEKttpJMzTRpSa7aVVXBalB6yrRXu7DVFsuKVxjihwt5PKqJZYB7JXFNSHkw0VxLf9ClswxuvveN3HnH0dxx7zTfur3ktrsD7rjnaO659dd45bM3MZWOICwfPdgP/lIpfWfJqbEBIA9FtyL0p5kqJwnmerzwOS227juGB3efwvOf7ZjbuoNlRU6q2HHxDLz0BEKWM0ircqhI8WxC2s0OtWiZvb7kruXbOy7mmi+G5P43ufvWCY4+rcvN3SP45f9vDfv2nUI6WI1vhVtKHC9jJrqa+xZO5qqP3k9eS/nTX3sp1/zlBG+78i7ec9W9vO89Db6zdcCXbtnIrR/5jzxn8xLd4CGS9UO60Wa2Lr6PX/mDWbbsfRUXPesQGDRUz2bcw9seLDnjtFXsm9/ET7/ofrbMDPit17+HwcwqXnLp4Xz6drj1gfNYGU6xugX3zsO1n3IE/YxWmtF0C3SyBTrFHBvqTeJhHco/Ydu+w/nYl7YSR2/hw2+7gL998yJu2znE+W287+/2csf2/dxz/xTHT7a4/cuX08sGTKzt43lHUhbrRngWDfCP4c6Fz/OOz05wy8I5TKybZ/be53P8xuPZunclW/aV/Kd37GPJu47+YBcTTZXWqV/rUOrlRmZ2Os7b/Hl27DqeZ18x5O7ZmNf95xXM7jqHFz/7Fj775W184cGA+qo3sHb1tdz/4NP5xqdeRUP9Sfld+CygAN/D95xCQYO//XjMLbsivvzARqYP/Sr3791JLR3y+Q/N8/pfEo7OCt767pR7985zyeV/z4Pb30mYhszOepx7whJbt0/z3Fd+iO/Mw6v+7H08stDkpeefwddvajO14mQI76Nf3MPNs3v5xDcP5bMf38av/PLHuOgXZrhlxxHcO38Shx72fC5+RsLDex7B9x5k27fP5qdOP577HlrJw/sdb3rjfZSz19N7aCdNg1AdW7uP9/MH98T3vvJ43xu//r2f/n/1r9BCEN/jIShWOoqXPvp61dI7xjhgVBts0sQ+KuE3avvVdx79nl6vOvMefclkc+XqK6ziG/6GsughhTckVTlRLOwFlQbFJjIa8UY6EWxYuZGvff0hFmYCymzCBEnaa1rMII5TPDdCr3NDq4sVUMi4OUPlg/KQHnWHrGJft/VJBHeYUWRSSFVUPi+GxEEH5ft++uKfZ2bxJPJhnUjxz7BOr7+VQXorf331b/MLLzzd8JifcPFI/lslTVTBZ/oJQZBRFqpXX2Ke3bzkshN49/84miSct/JD1x4yYNq6MAuvyYrpEyjzgv5QNckL9MsOcUeVDjMwTBn6Qxaoc/Pnr+KY9R3CHSF+2eOszYfx4b87hThOaIU1EnJSha/cwDgom0GNO+69ik6e8uLnrmPwrpfwGy+KKfIZ5vo7WQwX+aVfvQ+W38wwiwi5jEG5HziGopiDYpbfes2v8JpXCy6zAYnyHF1cv4M/Afu6GfPDvVx68TN473vruF7Am970y+zZOst110wSewX1ycIUTuBg/eSQmNaIuEL52oBF16ATZPRILKJ68y1/wtrlCVNnreezX/5jzj13J/fe1eadV5/Os174ETrNSV770qu44ZNbWRj8HtMtH6+eGiSvmnuCekaujs9IibkeH3nvL3DpuTWGbqfd58mJPTz00BRHbnwzz3/psfzxH1/OS6+4nBXLoTsoaTQahoPiJQnNXsye2RkuP+lUPvSB9eTxIm9441qGgwZv+y/30U8zJjpLTEZHEMdLrFneoNEq6ec7qIWTFOU0LT9m5Rp1Efr84ouvgeA7fOWbr+emL/8lqt5T4+dC/26Whnfwxteezat+qU/ozXLNB15E7l9Co7OawZaEPd0JLjjjWD70V5ey6GW89XdeSveBIe/9qx5xGDO/q2AibHD9Px7BIe15/Mij1h2ylA34h2se4fqPfgGiRxjMXYwrj6JMY8JoDxtWreRLX7+dUzfdxGmnb+Qv/+rneemv/73Vl3fDvbSYerTh6sA+UJBS60yEH4r3VXR5FvzWh0yGKLchYSzsl1GXoy+kP31+3IQj9MQu5Gurfa3KMeHZmLCRhfVk+aEDI/q//bcnkVL/GsPXZH//Q6dVplFWbzWEQnFtEhpKvDgty5haVKfwd7J//iErRbv2Uy/j+JOP4NJnfp2H7q8RuMsJvWUm2BU7z3PVdOdVLbNZ7OMYuWLU3zeO0Xm/f2Q/+LdHEPqkqTrjctJkWJ2vhHYr5IjDU448osuatTs54qg5jjve56lHtWjXTDX84OG+/xU1b8lrUbm6TYVqR+SdQKTqkeBmEu4yfZIWewiDHsT3GQ542NhHtigOy88wPbHdVOKEt4K271O3uu5dBIFjalL4yvNMrVgkczvwogV63MfK9buJY4+8zPGjORPsZT5N4K2i8HsM2UYQRXhBjZIJorpK3e7Fy5ezeuo4yDs8/aiII9fu5xnHPcgpJ36bNSvvJy58mt60lXIGtYI8UNJv3jwe6rN4nT2k5QxTK9WVsh6vMYfn340X3U/N6xKHIRn76bpHCLwF/DDFBUoG7mWQ7WOgfg0lcQPRyIVG3+aFHvXaJGWwQFG08f1YaWmrjJnoTDL0dvHm36jz1E7Glu+ezIZ1xxC3bmFq8n4CtuP11dsXUw52EnMPLt1GRItWp0bituPKkLixyNd2refwU25kef1yrn/3T/H0ZTX+5D8NGaYS0jBIBgy4na7/zwxImI7q5NF38IRC524nDGChfBd+cCuN4XrKvENQbmRub9P6BYqsTSPYwHC4hjJrsDCEt1w1z6vf8E8cfsSA3sIVnHtsj2c981ay4SP41rW4jLK+lWLZV/Asud3E1YZEnZX0ijtxzQW8vEm9sYrMK0n6qmPPqNdjhmhNDJhYt4e58kus2CCyBuUbVpKWh1LLL+Sw1adw2IZDWLNqGU8/xXHiCSW9/R2anMjXtoUcs/l9tGunc+PHzuXYVT3e/IbdhI29tGVsqEvUKb8l6akFrmdlDKrz1SrLrDChwkCXMWiepTVRCXxrtGFUBzmy9qy7WIQQhjAoD3KUPzOgsIMhF75/s/34/v1/RuVIQNsNKwxpzgA6AzXaCr9iQJ45mnGHwPPwy6fRnpTo0m3J+PSXXs4xE1/nlT9/NyqTiz1VMahRo8IhqVqKZbYH5HmKr51h56rucoUHPL5hJhnHfzzmzyJXV5mmSfWpHq2oRpL3iOstvvGtD9rG09l0dHWE1SIlHofUItWMq+16DDD0mIc3i1/ldjZG4USopG1UFVOBBjkmVAuniFSmbriIiEkiXy3rUyyb7tBqNJmbWyCo52Rll+7iFLd+PSaIDqPeKMiSvAKQL5YoyoB6rU3TW0a/q2RPYghng8UlC3WVeYXLkqaOslzi9psLznxGwVI/5N7bSmu1Xlzq0S3utDrb575gmt+48ndRWdqtX1A7trr0oD9wqAnGCwtybxFf9GrCcY4EfNQlG06aR+DHKfOPZGzd1uSFSQlCAAAgAElEQVSIQ3y2bvdZmtOcbSUKpm1sEihlqYBMju8PrJPR9n3pU/gzNPw2WerTTx7hm1/22PgziUB5+e63Na9LVkP8wQ9/kL+/ruTpx76a//HRZ/KUozdz5Novs2xyhbXh18MWHkM6rSmS0uGKJkXZI3cZdW8Vw7xC4bvvrgd4/8d+gUEffv9VKdu29XjvB25k84XH88KXHmX42ZG3gTJu0nUFybBkJo+5Z2vGkYdPsWs/5IMLccUNJOk9lOVJTHRUuSGhpcKkzHDVXV7VeddrOQ/cvpwXXvFKfvsN8LrX3MuW2z2++aUH+dkLbuLWu19O6hWkOdx153oGA9VOr+SeeyP29uHMMxfpugk6UYOtWxMeeLjPkYeuYPu+hG3bdhNpVrMO3b7qtetW+5z6FbjrMJ7D42FO3ryG//7BS60ReMtDcP/9Wzj26ctxZcJXv9ngun+6ivlHCv7otxz33B3xzqveztPOPIRXvODfP2rTyrPVcxzqszWuUoMRCuMYIMt2iXnd4+qxUfJfBQ7aZHrP5IfQL/THGFNkDEGg1lztodFnq23/2Jvvx+jVJ5dU/yoXM1aLBx9MlrNvbasShoqoBkJQK3oGha/70lvaT8lW4nKaPNuO5/YYJvGmc+8icMvxC48sEygQ5r4J+0H1r3KVhJwna+tA1YniHhrHY43l4HF97+/qLNOCCEOfIlelS2a1yPPze01IJz1H2ivMRijzHLUjx2GdpSWfQf8ANsP3HvXAX1Vopqxqp7VRx/i+aiHPhwTFSrKemg1KXLrGBHS2sJ5cLcr5eiNqmJlfT9a7kOuv38f1/7ib1/3mDq55f0mRHYdf1si7wmVZTd0dQozawtUx2ibMV5ly9NX6kK2k7k9gTY7SbYVHmS3n2afewMc+GPNHv/cAf37l15iYeISTzhyw8ah548z8wz8IeNOVO7jm3TUuueDveO/VN9GXdxT7lEWd4bBPjdVkpao3VJ7XIBdWjN+hWY846Zhv8u2bvsLLn3c/N35uOf/hZTNcd/0/cuJxOWvipsXd1SovnIs6x9IIJk1Qp8IBKSuwI5Wz9YRd3pznl3/uq1xzFVz937q87tc/y4rpWzjy2AH7Hj4K4l1c8oKc7bse5lM33Ey593Qe3rKegMMpI4VY+iTpajz/GHKvRUSTWmORIVsJa0skPbjw1G9z8fFfZOeubfzeu+7gvOfvpfQW2LVd93SeMnyQ1Kna56k0V9Q49wKPb926ghf83B18/oZDefXLHO9/13GccMKpHH6Cs9byucWbWezfShCKEamiAWu3sZAU3MaVr9nHOScs8hd/8hD/7pUhL/mlLvX2PH2mKbyQw07YxYb1Z3PD+4/m1S/bwxe/ssCLnr2d55zVZzB/NPXWRo47tsvWrffz0ud+ky9/apHX/Op3+ejHvsBRx+5g45o2vb2r8AfHs7SgHSIArpQNx/Q55LABn/9wg9/6d3u44ZO7uPzcP+J1v/YxFhfnmVvMuOIcOOuQHezdcT+vfcuQC56tUsnzWNz5s5WzbEK3EtDjHIxZwNqG9l6FfW87Qlghhhcy+pJeNMBz+8U+ggFgVVZ09cLBIkwIfmMo2YOOUX3wx/rffyOL+uDJHM+XNGSJHyle5VFaZ4nasXXjQkvKBMEiHt+lyB4kjI4jS5do1Lpc+5GX8Yqf2cZHbvwqhaemkBMJ/JoR2XqKLQr43HpNdENHGMTj0/6v/nQ+WZaaMghEmiDmmyJj1bJJFhZnmJxYTp4HLC2p/lexs4Je2idqiutQi+WJhXUV7jhoUPqK51upnlqeHfez0Jsi50RSod95IYPBFns9zReYmprkJS99On/73rt58c98wuqQ1605kU0nn8hNd9zOoHsEnttLwV6G/WUWUkmLvSR8hxKBXJ1lJw+Chxhkd4J3Ij5rmFm61WraL/mpdbzshX9GTpupyWne/teXc/5FbTxeQC1f4NWv+SZvefNd+EXGf/5PP8vr33yk1ZkXg53g6RhrKDkezx9QBD1ibxlFIuuxYNWKGtffeAm/9ouf4sbP3M3PXaTCvwbnnL2Rt191EscdHdBNM5ayG+jUVpP0T6NuHZmaF7VpR/h+NVfTy9rk/YSfe8FGfuc/XMUCu5norOLP3vEiXvTv1/KJT+1ixfRa/suVH+EvUBHzEs+96EQ+8dmbuPrqGU4+4SJCPkevtx5Q8m4nOVsZLB1pyq3MM+rNJf7dz57MdR/9HM+9QM03h5GznE0nrWTz2TImavjhcsPnFr7G8lV1PnLD87no8u3882e/zM/+9OcYEHPcyc/lAx++hKcdXjUnuWAbw2I3njyEPCauVWw9qs0I/A7PvWIlN998D295/dd52xvmKUp5dyEvevmx8j85e9Mz+fDHY151xVb+7tpP8f7rhDXydJ7/3EtxmWeJ7+s+u5aXv3iWz37mi/z8JbfR8wrOOu0ZvP0953HkRujPPkyeNlmhaEWuWnefZ5x8BO//6BG86oVf5X0f/Cof+CBsPnM9//DJX2RqImGh1+CKKxb4p498kuc862ECVpCwkRM3NTjplAXirGMG74HVLemsa6teOWBhjz5hb0guSNiOLeqRQJflbZJe+CzWoGHKxFAsR1+vvGVZGeMXxr/8+P8Mrrzyyit/tJfxGL6HtaArKVARbFqBuvk1gi6QcPVxwqFIC9YfNsGZpzyDtWuWWx1zt7dIpx5z/sUTFNzJmWcezdNOmCQ0klHhTYy1twIqwnvQ1aktW67SQdr7h71odYOFQlNTA5dgM30KkeYp1pd08YIJa3gIQkFeeuS5XP6CUC6/6xJ5o2TqAXPie89sgllt9pqnahFXoRAJo9ISOBdfeCJPOe4wGs0IhgmFv49jjtvA+ecfxXQr5tKfPow8e5jNpx7GppMO57W/ezpXvHgK37+Dy559As3JnHWHL3LxhfO0O9BoTJAyxxlnTXLSpmMI8nn6xU6m1s7xUxdtYO1KRzfZz9kXtnjrfzuK/tI+Nm/ewCtefSyXP28llInhqJxyap2JFQNOPr7Bzz5nFf/xtc+EUgHkrXj+FhrBAmdf9FQOWdekUSsIooE16QRpEz9I8aKUTiflosuOJF9ybN7U4bSTV/B7f3QERz8NekWPdpSRBTOcfcEkm5+5GpcHhFFF7Boq/uE3if0aV/9VnR3bp7nh6+tY1lngmBMdr/y1k3nhi55iiuEpR6/g6OMC1qztc/am89l8ygn8+QdismQL+fBeLjj9HMr8bi69bIqnHrWCmJCVa77DueeezFSnSTPsWPff8168gv37t3HOGRdxzrnHsen01fzm75zA2WerbVaNVssqvOgiwy9D6mXAmReW+OUOnnXeCk47q8d//MMpTnzKFOWSeBOhVg84ZdPhnHnmcRXIv5NnJi9TCeGVnLjJY/WaLRz31EPZdPqJnHzakVx40ZH8/u+fRF765IOSQzeknHjmPhrtkGdsPowzz4A/f9caynI37WbGRLPgwstXkaYznHX6Oo7Z1OQP3nIuxx3n46UZzh9y/EkdLjz3GPxIQFqe4dWsWjdg8yXzeNlqzjz1TN7+rvNZuawgyUIadZ+fvmLIfPdOnn3O83jm5kmO3/xt/vObLueMU9cbcqWtawNpUwhDglobVFetHaVaZ/tRhUXG3q48X7Ou1Ssxtp7HJrj+ljDWmBdxQn0sm2YUGbuQgetUocrqc9+73X5c//JUWfhvO3j5xZWLo/hU7vpE/gTFWIiKxUPStYzIFEKtQ5RLSHYFS0zu1ckXB7RrDRDIjtFhDawN1gvq5MmCadwwVrWoMIwrVL9Hgf1HCqG6ZkvbPfHlOxgOEkJfgEdaJFUIRQ03soYdiumWhGrUySXUdTi1RverShEBWVlI4zEUlo4mSj2rfFEOtXJwjKlD6R6t07gkLprM9XOanRB6i9RagqJqEeQD8j5EE3JVl1VkriqJUsVFTRgsq8DrUbgamaeG3gdIGRK7I+i5gJYfMWAfUbKbsHYIfSUM05QonsexxuA2fW+JPOkIr8ior8QIMxhmtGprGSQzNOsTo1jhHOTqGu0wLPdRF9JfHtJXqCJNiIS5HA1I+i0aYvsWVIrXqzaYN4/vhN6ncsqIzN9G6oT+torYlSSeKkrqxM4nVZdLXfjSUAsGDLIGtXCB854R8d3bmty5EzYuq5ptc9vbar1z+L4Uf2DJ08KJWV1CfoCn8kdhpSjglpZ4YYPCLRHQgWBAvxfSbEQ4f9b6PIfDDvW6WrBLDJRfAkW46IXY0icNPVCpcHXllf0GgcbQFOpKgbG5yD4JB3SLBZrlGkq/R6iuHF1QqQ7YhDxXo1kd32+R+fO4oU9DGALU6LNAYM3mU8aQXnoe6WBgwl4olBXfZYIv+NgkgPp2BllBwx1Gpk7G0OG5FpkunyXzmoLuBH4ztFCFn/Qo432GD+8P1xj+S9xWp6L8Xh/fLeEJOrhYyVBjjnMDygq8/dBfUfH/lipO2kbh1qKm9SrkWBlmjxpNFlvWeq/2hQlsk0TCrVGuSiA2RdWdbB3FI0FfxBWnpro6w0es+5NMXpBKgzN8pdRN+OvzUgZmqT3xHv8xePff+Cq0akfa0jRrQegL+UOxJQHbBGRCkpe3U6RE7dy6zYtgiaCuDjQl59TGHUIaQqJqBmX2tYgKinw3Yb1vnY5FLnjTquW9Sj58/6VqLE/+UGKz3qhhbN1SIL5wP4TsJ/ddybVFIlmHDAiMwy9DLAm2KcUb9iSPA6GP6rqrjztjpVG8WCyI+r/TUfGcOjETKLWQxaiTEEzsYcntY2C9orm13FDzSMs5crcAecswVdR0TH81frnMXI6a38MJg5M5wloPJa8EQRqJV82KZAaiaQR2EMYPGb6I0aflPu16n8XeVhqqD+sLAvV28O43rFIJgLCxkjxZjoL4npcRio1nEJKlwrhomkLTcPy8SdDN8Ut1mt5vibtMcs9gRmeIsgCySVSkWbJk44qbOmxGLKMqz/BDCfQAlWkKOtRzAscaMuxmFTZLWUeo014WUlMJp4ru2U8ZfAe/CCmCeQpE1qzk3Sz9bB4XSNlo2us0WxIci6SZ8iPbqMdThuEx7PmGqVL0w0pwhjsgmK9IBkpRvM0acqO6/ItujitqpBZ/XaoSzMEaIdja/AhC1AlYSpyEeY6MjMBvUmQlcRZTCyes/VyVMuoXrAlVJhGZQ43Q9ak15g0ISko0YA9etsW6eWV4lmWPRiTFBFGmBOkenKcAjNaRIKeGRALukF7VlJYZodchV9glUku8BPuDeDxI4Pp4uVovlDMoDUKgMSxMGZXFCovyCaLWC/cSlhrdQkX4rE5mGU1O61ZKSNVZ2vMq0Rvtw/HyN4mte6TPVUrWzO4R/IOqR9RnUBo1XIU1b8d0DuuS1pYfH+tJ9t6P09s/cotac2b10jIZRBWkF8YyUpoz0CIVH1zDLB4JLpVjFeU+/EDARxP0BkMm2ocxyJaoRy2jAcvKh6hHKmWTFS2UssiSh37okbk9RJ4jKzrWdONcyzAIxuBHZanwRI4nUP6R/K4ci/HAxrdQIRPVfAvqs2lut4DYhGyn5p5qYwhr2eGFIa4QQYIsLS1EQcDKCvp+BTE+dvVTCkBx1u9/aDxWZ+p3rYTLV1zOH5rAEoKdeRLIBe1Ti+fJ88MJA4E2VfjHzr8frzwUlzQF5geuZ4JNc+3V2hSFPigEQ50ntZBTkQeGNmgOjwqXvZxCAtylRAKNVoIz7RvTSq3eoVSDSVInCxcNsyPwUqI8MEAqzXUSb6fwG3TKteYd+Z29+KK7ynosbXsanY0al+6/o4hUYy0M42omhlaat8EcWeFABypLLFZVRAFaR6HqtLU5oQgS7rxpLYtzcOoFPUrBm4ZNhMcSsR6XK6ZbmDJSBCxlPzVV4+SOJG8b1nPudluVkcckpbnSEmIpoc3T3qomtxRa3SReNGv0Yy7cjV/mDIo2NTWEEZOHKWUWE5dNytr9Va7FO5Ki6FqNdpkvMxhZYZrgJvFUd+6tMMOk8JdIBRLVWGlwtvWms7p0hYlib7URNidZ16znvFBjl26JhFoFPxAE2g8zxiwuFEqS5baGxJHZXfDpLBclnXDYmvQXfVw4S1zoIE2DwQnrAWWxgCcAEME8GPeYKlFii3V7YRvlcMUEpn2cF7lACatzlAFBlJlCsvekMMsGUU09ARLLFXWaGIMUYxf+SyBseHUIlR1bW4UqkGwvzJKXM4TCYyknR1DLAiFz+LWEJNtn5X61uGY9loNeRsOI6gPSYUCjIYhX3d4FPE8ogJ6FKwNfNVXCYEkIqSHGHFmCtsdHckoGlxXWCg9FzoBwUEYMUYbPP9qrwqWxeTBhpmvQOA/axarpNqq/xMhWDDfbkDqUH5F8UNGDDIVFW0vjmm/NlIoIBD0s2GBP7OLyO37UoY9q7AoxKORRufamuqVJBSweiKm8wlGWJhRZp0B7BNkotz0uO3h+TDKcplZXVUhkoORBYwfJYC80UmpsAKsf1VzJuq1ZrDgMVSIXkWVqoFHlhha1/KLq4qVADP3voPk98OtYaCsm3TfEM1mBYq/2IsU8BPGqu1vhDlff0woWalpVE63uytE8Hzjs4/x2cASqildXHzTrwHBKtCHEUaglH5qFL3YPLbKknKXmy/0rzUJSCFCVEJFbQS53uNSNV+OAYE1DwrrZ6SaohcCnBabiFnvIbc2EHaFrk9VWp8h6hhEdxcJVWaiUUDltoUbhcXt1n265i3q0vuKwFEqbW7KkK/4kBvyT1ylbgjXdSjMMSWY2spQHLJvqGP+t3RcvGxEvVLHbTOiKMuudQgdz+G7aoHFdrubtOaJIJqOUjeZkGpfXbfNkyRx+XRtiiFc+xXJPCHaVCfJSHlluxAHSfQjcquwRCHQryw3s31NDkxaubeyuVSL5VheuEkl5cTJGHybwpyGdIY2bRgId+FPkfkwoKZEOGda+QVSsxssPtdCLC/fb3OGWkyYlcX2klZT4rm6PhdO6PSHsTeApLNLcj18qzDFVQa3GHkXuWyhKuDZe2TQLUso0CNXwpfsj+jARCgh8SUQTJbVYsTuZmwFJCt1+xrIV8qKUnxY0sDDWCwM281FZnmqYpyqsHIV5MgnhZsWq5KtaK68Id6PMcg1kE1aeGsQKQ44a5KRA1CcQlwYaJY9TlTUirfBoUbjQPM90WCOqMMRsPKUbEAknpBRUrASl8j+xyQmFFg3wqhxSV/hTckQIlBpP6RGJ0cONyTVGYGajpa0fpncf7ZCWOJAfUTXgVAI7MIu94u6sSm6teGx0DCt9GMXYdWotE4vUSpRFupGCv6zCSDpXGJa2d8zQ0plk/6hW/GChPvpdnoIc9sq3GSVTtUZ11H9TQW0330JP5rooFlsRrcqMDgzuU8JGoO0qv1IdtCvFFhIxzCFR6VtjaDGqSNaqv0ikBF+5whaJQsiFwOglSHUzRHFfiHagqgwI4sysXbW0ixIsDGVhVIvKBKLN+mhirJQP0lyJQcXmJIQrwSx8yywJrJSt9OXmKtYul1HHUpikEtS5wjcm8aqvPt6/Y+tZAvp7hbSWhVx7uXiiERMhwH6KUqWMk4QWEF+0GlxfQqOKJ4wUkapdRLOl7syKKVwKMfCjSsFIRnhKeFWEwvJfRchgUsgX+YISWksE3nQl6DQ31rShWO0qi42rUabeUhlfQOI7hp4aZyLyYom2wkGWfJ0gTBWD7VE2RIDbpUgWiWtteqXatg/HU3lmrqSa6LYj8myBUNZbKOYeueGejUFVBVmeGfmtWFM01mSo89Tx6wL4h8HSMhra9LESHI48rxOGO4EtOHcorjjShHmZDfCDR7CwGoLDWyZkLyOxsBCEa+B76nJT2EChAW0cVd3ssvpvK28M1qLC4yTeTeQ/gp+uYyGdZKIVQy/DayvOKj+9SnqKnVn30/NG1pfmNNzCUjJLp3ayNQTJpe8v+UxNOYQKiLeDNHHE4TRBIC7HmCLrWk2/YFSdqopKjVV48goJCAZXDVSqL5YXWzExFd79FJms2A0EscficA9epLDURlIx1wgZzzVpSfaJ4Ue1yLkIBnTfZR3KdYlH0AwJReZZeCaPvmUWaJCdXFVj+HutMklehSvqVm8exTlC/Et13+viWnRW9y5WGCX8VcmkZjKlz2NfDEwiVBATlLysJfIiIJbpLns4E8elZ2GZhf2+MfaELWFi1wmDBknaJ45HHJbStgqN2n5Ume2oVn0kME1GjPZ8KavKkEGrYgS9p6fe1sOm0sicZexVxlJVnCCZ4/Csc7niFZVMEUCdoIvVWKSMhbqPZbQJl33sQMtyD6w8TZtR8sVijnY+lfmK7nDsYY5UejWYH8W/o+s9cGgtJiuhFoO3pKlmoSDP1DotE7RkodsnGdaI4xXUojlba3UH0yKNKBNqwRKxrwlQY0LdEnhSpLLGY6t3lpBWXFBddV2bbZ1GGWSbRO0PZQc1oSahDwzv0ZCZvV5i1RwWO69cTC0qTapZANL2ohRT4kZa3w6TGgOMNKaaUn7Yx/cLaX1PY1P7gjZvkeU4WVKeTy2ILAFVKEavtWi4Jlo/1Qi0gDITupV1UC2CcQmkzIAC35h5xDguMlQJaO0XWQLCxxY85oAoEgOKElzVgpXiTLPAiIrlMdcnfPJsSG+YGZTnbDdidw/6gc9idwIZcU6uvX5aAmuefuYxLLQ9Z0jLNvsTIeCF1Bo98p68qL6B2yvcpBBPoeSmJyplrXLfIFsVozbCYc1HrKSbwru6PyJwWKQIHC5tkS3EFg6T4NHT08YV1Kpi27o1gRSwBF2rAneW0BPIs3BsLEAvzHRV7TQoLUSjRpD9RmWluUqzvnluS90GvX4NaW7lM7IswavFpIlIG5R4FK2YTKmmCWm51JYACOcQFG+nllAUA0I/t87TVq3O0qzFFCyZliVq5pGHILFRUZLJW1BDkToTA2Gfe4w6c4V3omSrkCRFBxeSKUQRiCJO0aU+vWwfUXNAkW1kTqGMuMVD2/sifmFYZIbjLdycRBDEpSOXgPYE7ytBMqT0M4KaWAFEhTDHcGhcZraG1HgkISsC7HQQsnuXsMJFuiEShykT9hLKkRgFFFooFHFPLY9R82MKJwiCpFIShSBV54gDUXs1qlCKLy9ASniObJjTW/KIowHdbteEv+/aRokmgpOqRldt6lIOlTdvTOkio5A3b9UllXyUxWvk7lpmriArZRzJM6miAfK+PCkvF+EJjks5FhNfKiTQPpU3a43x5tGoNd4TE49dn9aZGHWqpLrvqp4RyxvZGMZVLlXVmPZrqK7kg6TzQb/aPP+I/hmfRgOqjDYTTL5ijPshkIUga0MkAh4tUUz1a9x80zw7tkfcd2/JPXf3+c537mH7Fo+7vxtw5+1z3HXLJKVoscqeCWRPsVp/QKiEmFKUXsU8IUNeBoZwOuz8ghR1isPpz0ptalha7Lbp7ffRQG17KKZd3VgJf3t4cjOrLjxpxrAKBONEuSJpr8jJOHryQ8zqwZb0+OP2moVtdOMyAlVOZE3IJ6xAQArYleICrMIX5jf9T97eA9yustoaHqvvenp6IYQQegk9SBOpIgiCYEVURGliQRCRJh250kEuRbqICiqRGloooSWEBBLSSUKSc3La7nuvOr9nzHXi9Zb/ev/n+7ybJzlk731Wedf7zneWMccg1lo56Og9pJJLhuGjFQ7B9RqIjPUwbRbGepFEG9Q4M0JIWCSiIrdJAnxH28rVs+JnCfUbmZ+iVmO3Et43oopm2kI/j2ybicUrenHA1Bg7FoApxgC+f9KgxnGOtQX8OjkYgP7ecdh1q8fw8bKtSRaANWuBLbItfPeLG1GLlsEu9OvYSXMMwkYX0JwMO5qssDdVR6DiQFxCEC1U6Scu42qjhpDRjBsiIiTObSr3dq2hWbU0lA7HIQmmA5iqi9F0uAjJaT4ZSLaCRG3pXmYxaqNBoIgBuWdqahoDevwGFXVMmMmOcLALEmMDDHcY/3L5J5jW/gc8cfckIOhAK+6DRYgMF7E3DIM5a2E+lF45C8CcXDwPDdLHsOPdgGim4qX9VlYjiPvumotvf/EJWHG7Yto9e3tknW0QqQFmR2o3knCs5tyjqKB1BBa2bbsG0y7pdYdJnaQKmiYzTBvV4Wlo1nv0Mz+O4AeTcN53BzC9EGKSsRp7T38KHy3khsoVQeqCEJHVB7Go7sS5xD+WSorRcHNuBazN4BBknb11nHx8AsdrpXwxcTdO/drj2HunX2DReyECcoZnmBqgVmZ3GuPbgykgJCJR14ghZY1DBM06ETkZOPQ4abytTYDZCzBKJb94tRunHD0Hu035M/o2TkB7+xjNrTtegoRIJ+7bJjct9jGQZiClQdZJDA+xikandkkFhs2ECR69D4vkckZGn1lK28zrTh1DIru4rtnvoVVvFuTJ1YMEHhukuNnHCUxVaXdA35MbEuMA9cDjjDahGSzkqngz16uDOGL9zlT0GH/ST6T3vfmVjs7mf/0zfqoTk+74mw9Pg0glaSZqDOWs5YVREFY0nGefyHNPvYVzzroEpepMbLPl9hg1aRVq5RbyuTb09zWwdtUW2GG70Xhj8WHavFCl9JJlwqUSi2QQKhSL/Y5tCFUJmskyGlCGJIkWjvhUFHfN58XYWwszfDOFB6rl1pwSW5fpvaf5JXpkLEg4TlFZ/rg7M8jh4JoOOwaoaEoPmKmV/xkn9eax+fviIj1qw62qgIDDjcC2kESGKolQ/kvhfBZDRVvJ90lYRIOgp3fyaIGCvGkY6Dj5VOSU6jekX4rZYZlFgKoWcNSTlprmp8m2RwkzcnzbFHi14hFSKkO9LLFd5N0CysEmtOe7kZg+Djv4VxgunYdd9vHgxRMwYQK9Uaa0fHhdXOwOjj/0JvRt2ApOdxkm2G3ZA9cBapUssjY91y4tAFPo1kEd9XKInNUBFrG0rsEvJy0ViyWEjbngXIEdiyaSIINWaMDI5TUN4BQZaSRWVZcAACAASURBVAhaSYKMQ4+WG04FYVyA5RJHEsPK1pTDhJ5UK/Bhk5KgSN+I/ijrG3QEKhCHnlIRAZnxlDg/RN3PoejlkOsiFidARDk2t46s1hMiRE0Dsc1CVqD5VXpsNGuNVg25bAE2+DzGwKaKrlg6j+wM8PbbG3DmOXfinFOuhtgUwaC9IZIli5AEpmyjN0zyjSFigTgemzocmprJIE5q6oRQmo6yB4xdHNtDvoN6lgaa8TC8XDuuuXwR7ntoELtsdwg6uvOoNfIodMZwbdYJWCinMeqBpTA5C42WjUzGg206SlPQjEqw7QxaQUZTgNwoLLhocM5ZGTV6U7btwIyhaWgf68ByIzRaLRQyWTVAVG63yMzF+1DlJHqZ7NRMxaCz7VVEzRZsivZqCoMWL6PFlNhowixm0YgyaCWTkO3k2DY0uqMMnQ4Fj7t5o9K0Mes5vC9yBNkjkbQg5GLRTGlD5eboP2vkzWKqfkYTacKPqspBw83ajxK4lqvGn8gsInqoZM9MFYv5Tm6k7uaxaNiCFumZYtVaBNOxRLNlYUsR9I+0b8Kmg0EhDjbNedr8ZCvSJfX2/vmG+m/e6WZTxA2JS4EXQGjWRE1H8EYdkwWBJmxbcOJJe6O78wp85ysLcPznDsYvbv6iGsM4+VgVm4/dZwFGjTMx+/kVmLRVjElTDYRRN1Ytpwf+Kvbbf298OD9BKQix3yEcfh8ZqxNB1EJ/7yBWLNmEadOmY/wEKsuwGYNsXUwBMD/mpnL3bNPW1DTz55QZSkWVxfEhUQF/+eMQTJfUr5UUkxsTN1tEozWEXXZvw4QtQ2T+fxjq/5iGabVaaNkBXn6mT8NGGokkibHjjG6Mn2CjlYSY92o/Yr+BQw9n/pLOWhtWrt6EhYuGsOvMbrQavVi1agAH7P9pzHllBZgiZLh84L67Y7BcwiuvEz0zhGlb92DqFFbLa/B9H9wYTLMd9WQNcmY7BkqdmPfuCoTxEJB0Y4ddtkP3eHLWzcfLs44Ehi9AYfy7uPo3W6DNbmDGNHqwHgKiHcwYK1Y04ZmHqPbjnOc8TN52CGOneAhCA109HWgEJt54rh+VWgGxO4j99huNiaMb+vtGVMBzs1bCdSx0duWwx767oUVDTXV0wvakDUkYI58dhSef3gSn6GPffTsw55kP4Mt4jBttY8buJHpaCzF2wMsv1FCpNXDE57sx92USXjVx0AG7I0KCTzaWMX/earj2ODRbBvaZ2YkxYxw0EubVO1BtAu++Ox9RfUtMndYFr62keF7J9MPCTpjz2lpUhxdjv32nI5ulxJyNUrWG1+e8jylTpmDr7ccqieD8NxMMbipA3A81b7v/p7fGcK2M+QsT2DgM8z5ajdkvtmPLLZuYPNXBOwtWYdP6Lm1Xt4wEhayLgw6apBqTLJCyyB4Tsmrn4eWoPWnildmr0WjVYSU5FIsxDvz01igFNiolD0veG4UQ63HMqQtwyFF57LzN8bDIQRgkcN0Y1WYdr744gB12GId8PsSbb7wPzx6DnWeMB5tgAoMInh4k7ljMenoBPHsUosBDHIzH9jMcjJnSi6+eMQNHfmkbtI3zUSd6KGnDMy/NwcQJHZgwenvMfaUXW+9oYupW49Vjf23uBgz3MXoVHPG5iUCOsEgac2LHXXy0tIplSwyI9wn2O3hHIL8d5UtQC4BChkrxAaJWP+xcASs/jrFg0TrYNsnd6O9Wsfc+01EsMKoPNW3Ut7GBN98Z1LqWYdaw+17dmDguRjMeQtaagKjVjdkv1DXvH0sZE6c42GmncbqBvrtwI9asrOEzR2yLjxYMolb+GAcftBu4ITcqwDPPrwIyJey0azemTtwCzUaoMQnRHK/PXY7AH4PBQReFzgSFjib23Gc0YHI9pWkfqjkxzeKwbsPXP51KW4nxeRbKAcRKyh7GkQRxQ4KkpMIE5PuOyftNMnl/UJr1XpEk1l+555GNksucJ+ef/1dp+CLNoCqBfKzHeevNXnFxiey9x5WyYNkK+XCxyMztfyeOsbu8M3+JbFm8WVzjfKkklEIIZUN/ehW/vPZ+sbG33HXri9KoiPhNCh+UlYI+paEvpwoHIzzkUauZChDEIqVhkrmXZbgmkjXPFQv3iGNeLcAPxMTFYuMGyZuXya13vCtNafyPhpek6f/xFYahDAwMyE9/8aTY9o8EuEBs8yIBrpAzzlogy3pFeMUTivdJt3OjtCKOblP8QOTOO98TDzfJHXfG8tXv/FpgHSNXXdeQTJbXeYEUiufL44+J/PDsOeLgMgF+KCccf798uGxIKK3gj9wuR22oNSyNWOSKS1aKwWuwvikGzpdvnTJf1jdKEspyGe8tEgcrJZv7QAzzRtlq4hWqQxHGgdSDWBIJ5aiDnhMTv5e2/Hzx8JI4hWPlzVV1AYbkoJmhXHjRQ2LjG5LFvQJcJT89d51s6qNIREvue3CBZPETMfE9mdj1E3lrbkOiWKQW9kk5aErYpGrEgGpEdDq/FRMPyPU3LhEHJ4uHe+WAXZ+V5R81xZdl0ohE9tjpccniYbnxhqZYxqlS6DpEeIiNwyKnfeev4uIyccCxOl8uvXCpDFZFhgPS+Dflvnv7xMC3xMWZsu9uD8g5p68T275Obr71dZ2ye+9zoWScI+TjlYEMBql+xTPPLBAb+8rJX79aRS7eWxjLfnvwGn4plnGtWOYVcsdDy2XuCooZXCBFk4IML4uBm+SqK1+RhR+K7LzzrwT6rM4XG1dIm3WlvDC7LpG0JIxqEkctCer+iLiHyHPPVmXCKN7HhWLiVBmb/Vd5+YWGCg1ccsViMfGiZJwVksGLAnOmvD5/iVBvgzOxGUbyxtsUOrhajj3saTn92y+Ii9PEwcVy6QVLZaAaSl16pdQK5NbbesXGzwU4U7LOeWLjfjnh2Jdk5UaRI4+8Xyz8TN6Y56vQxNI1LTFwlOy3xx3y47PeERsnyjk/flBqicgb80Lp6fypAN+TDM6X3z7IJx9Lk/clJflgRZ8cc+xvxcIDAvccOffaF2Wb6SUVSFhbEWlEFZFgSBVHVn1Uk29++U0xcZvAOFOfI/Ajuf32j2WgShsQS70hcuWlFIC4WvL4jXi4Tc44dbYsWb5CEhUfEXn6sYpkjLvFwC0CXCR7zLxO3l4SSDkROeW0WQJcKJddtUa2mHC9AHtJb+8GtUvXXjFXLFwsMH4g+37ql7JyDTVMAvHDlrz07EbJG6eKi4vFw7Vi4V7ZfYeH5c23RKqBSDOm+Eldj9OoJhInLf3zzzfUIwaahoiTgOolQbxJ/ORDCeUNCZJXRdVXqDAjvRJEG6TeGBLqvNTDUBfQE0/fLBZ+Iaeeulhonlo+3w9k+YqSfHafAQHukguvf0YuuUQkh1gOOvRO2di/Qi4+9wPZYsxrcs/dr0lVdWYi+fCjfjnu8Ldln30elXfnL5O6v1Za4QoRalbQMOtFigRcZEldlW78xqAkVZ3BqjrS9PtlWBbLZdfMlit/9oZce/mrctUVr8pFFz0hl138ulx5wTxZumi1RPFr/9H+/qd/h9JQoxbx/nluSubwZyDy2nOrBcZDMnPvWXL37XPk6ksekq1Hr1Plwd/NFmlKKJO6RLbMpeIWtWijtGSp/PreGhlK5MHbRb596lpxMCgZrJHjj/tITv7mQsmiIRnUxcVDcumPBuWoA5uSwztyyzXcoERaMTVsIgl8bmIt+ckPlmiWdVxnSc4/91k5aMYA+0HlhK++IWuHNsnFFz0tPfk3pTvTLz8+pSm3Xv+6+BJL2GxIsxGpoX74t2UZO+Y56cg+Kyd/caFceO2D8vqGYYE7IHmslzH2i3L22e/JD8/7k2yzVUNMI5Kn3hK5+Qkazt/KdZevk6sufVG+/ZW5snXPc/LCnMVST96TWq1X/CARaaZGa1w3VVA4D+6Xn/54mZx4wDLxsFq22W2pzHu/JhKGcvh+LYE7LDl8LOec2CsXXXS/rO/15UtfXCtwn5Add3pQLjzvDRk76TYBnpUzv71J1lTrct3tb0gbfOlEJGdd9DvZb5/fSV4TXovkV3e+r4/tkJmvioP7ZMXyUJpNkXpLZM4LVK/5rfz07Hny1ge9Mm3Gi+LhATn+0AVyzqllyWOD2OatsmxDTU46/hVps96SXXd4Q84661F5Yc4bcsopjP375Zvfe1luu/Mt+f7pm8TCCzJpzO91vgTVfhFZLEk0KEFckVnPLpHR+T+LjVfk7LOfltN/+JxYVOhp65M/v/amvPbSRjlg11ekmFsjx+y3SS44501ZtMjXDdkPByWMF8srbw2JSaC5WZEp7fPkvO+vlB0nvSgZvC7f/FZJN9ENA6Ikph1uSc695Ba59uqn5PMHvCk5/Eruf+xD+foJzN2Fsmi1SLPZlPkf1KSAqlDRsduZLz84d5b86a/vyNPPL5atR/9Zvv7lt+Xq22bLhZfPkR58KL+5qSQiQ7J0eUkO2e9VyeNV2WO3D+Wcczm/V+n1tZm9sq7aK41gjTSGfPXEbrtjrmTwmHzukEBuufkj+fFF96n6kmnXZc1wJAPNsnz37GclixtkeudrcvlFoewxfVDa8YrcfcNGXeP3Pvy0dKAmObMhZ511n5x00lyC/mSb6Uvl3VXz5fs/KwmcXj3u0UdEcsEFl0u5tk5+claf5IxZcsuvn5Fbr31d9t+tV4486I+yauU6ieKm7LHtr6XdfEx+fN4iueKGuXL0l+YLk1qH7B7rJt4IBiSSFRLLGglr/yZu88831MmItJQEumBpCeKkIXEyIEnyiYg0xC8nkvgNkXhTemX0lprc2UVKQUViCeSZ2aF0OM/LN479vURSkb7WegkTkWXLIjn+hMflgMNel/33q8hJx6yWYVWDqnFNioGfyNQxd6rBr0ggjzy0Xnrwilz9c8qBiVT8BVKPFkmclFJDrdY6deiTESmhRJZKEq6QsL5arzeS9VKJB+XuO0R+9S8i11wXynXXifzqRpGbrhe5+sKmLJpPb3rtfzLM//ENekS0y/qXbhSppBAV0Ra8xYnwiOy421q5+vZY5q8QWbBY5LobBuXtlQPqUW81riFbFnslDBrS8suSSEn+9d6NYtoL5YHbEjnjx0+JjdfkW98YkFIUSH8SSZtTk63GiDz62HrdRn9500qxrAvlN3dxI6xLPd6g3iq9+qXvlSWDK2WbaYvkT7Modyby2hsiu8/4QGBeKE8+vULqgciuW6+TLca/p5tOEDakSW20iLtbv/j1ik7CY7/wimTwoKzto5RXS95azaz+JtlrWkWefnatNGlvpSannkbPsiqvfyTStd05ksnOlouurMgvbxc5+ZsiRXuJnPWdJSq9Vq21pMzTxBVpycfS09krYzpEbr3zffUe160QOfb4jwVYJbfcUOIuJJ8/qiWm58tPL9ygT5gBzZtvMuG1Unbb5QN54dWlev0vzP5Etpv2kVhYJ4s+EmnLf03GdCyU39yrboSsXS/yhUNXiuVdL3feuUREanLwp2+Stq6fyKJlDSnLkCTSkqef4jEukst++ke5+WoRWE/KiV94W4ZLqeTcTbdvkiuun6fXu+B9ERMPyde/Mk89Pw7o4Yc+Kaa5Sr53YSKXX5dILRb519vXyY13vKNyXqXygMrg8dlxLh2535+kgL/I2We8L74MS8UXOft7gRjmXXLwF07XTfSXV68X4F55blYgzVaknnkriSSMEvGDTTLnbao8tGT7PYflrdcGqa0nT896T6Zs8arAfFEWr/RlqNmQK64WeeJPIo1E5Nd3ihx2GJ/pXfLIX5bJd46iE1WW95dzLQzKog9KksOzMm3S6/LHPwRSY5AtNfnaN64T071Mvvf9QG64w5drrxdps9fIqO61uib//Pg6gTNP9t5jpcyf19Qo67HftqRzzEbpgMhHG2lH1olEfbo+f3P/+2LhYTnmsEhuv6mp3vPzL4v8/KJQhvyqLF7Ha5wt201fIn99ol+qMiyz3/xALr34JVm8cJ06STtOu1Y67Nnyy4vTqG7tQF2+9gX+3u1y3R2Pyrk/rohhDMtZJ4eyqY/xWCgbSyIZY1DGjK/LzXeK3H9XXT73hV4xzYvltrtekpZUZdp2Z4qH9+Siq0RuvCeUitTklhtEHn2oRddIN7RGuFJaflWjxiSpCf/883PUWoxjmYCoDuZQ0y5ESFZrd2yZdQuEUxFflwVxyU2fLcWESVF5o6SioRvXd0PC3TB2VC/8RhG5XBED5VXYcust8bUvHIcTv7IaERbg8Qd3Qkd7C8PVHDI28C+3H4YbfzYeV/5sDb7+g2Hc/5u12HXnfXHIYZQiDRVNQfgOoTdpLohFRUI9U/x12AphZwZgsFnBtlBvDCCfm4pybxfOP/1NDBpbKcrCtjphJE1EUkGbM4hszxRM2WkSCv9ghAk7I6qCJYzNiAAF+dgmttxuDH541mjcces4XHBGDQ9OBQ45/i84//KZyHij0ErWI0qooME8e8rhYERW2owQWXCyKS5b8AzOveBT8Kx+NJvdaIURxo6v4qQvjlVODT/+AHGyEKZ9IprVHOxcDrE5hDgZhmsXYRgdGDXuA3zuqB21YLPHTBsHHtqPee9XFJaYtYCNfR8g01ZBrToZ7UUW74ZTeFRswmWDCQFVG4mVXY/mcBsS0kJoMXkUxk8ewuGHTFLuELGXwoq3A6lXmw2quW8HbhVXXShg3S0RzqRC2q3JQqlja969EWyA7bKCaCKTS/D107bWdvqJWyU44ugt8Oc/xjBYcHCAwX7Sali44AoKtH6MNlKvmqO1YDhzj6k4cD+gWYtw8EGjMXX7IlasKCL0YxS7t0Nb7kWccspOKIU5TOoGjjxpEI+/NA9Jsj8QecgU16IVfYKOLlFa3EDqsN0OxFYfslkBu7WdZDucfOZ4tLUT2ujjW6ePgkm1c78PUcjaTQ15M1UrL5kRLrxkW6yYvxK/uWocIsZGq5Zgu22X4qxzPoNKshxeGwuzDmq1NmQ8YNKEycr8970fTdYmn6yZ4NyfxbjlzjkY1X6gqsU0ZDngLsRA3wpkvMnkfEQc5eA6FF0oKEwTaOKA3Tqw1166JHDIUbtixj2LsWbDe4iNEEbGwI/Pq+Caq/+Mm689FHPmdilm2HQ6EZhLYeY+B8MahGVRoV3Q3jYWhrEa40dNwTHHO2hWY7RyeUwevzsygY2Hbw6YoQXljyNMQLuU0EIEz5gIhN048ctZ7DoDKEcRvvilQdx8s4P3B0oodLYUBkCcOSkI9jtoWxxxWBEvPhfj6edM/OlBHzt97lxc+ItT0IFdMExRDkzHtlMm4bPH+qgnJczc21Girhzb7YlJMiejYT6Dc3/+GQw1PYzpBk7+1jo8/PgqhI0J6PKKsOQTnHJOBzpHMZdO4ZIixGihr6+IH529AQh6ENlLgGRb1AZ20nG//MZjcNrhPbjmZ+yOLGPJ6x/j059NcMLx+yiPjTbtWGMgVhaBUUFmBDr7D8xI+oD+X/xN0A9r6QqP4Y8UgKFGLpKVMGwqXIzTwp7r5VGrssocIG9Nxr03LMPZP7oVl1/zdVxw/nQMDdeQyRXQ3r6lIh2djl5EmI0sZqIZBhCzQ9U2iMb71un74oIzMnjuyRKO+koez77wGH76w22xx/5daKAEg91XMTugiIZgg0Ha7WewJVhcuARwy/bwGyw0ikIHY/HRPa6C3zy5NRLp1gYIgr1dh7wHoxA2OrHrniHyBrvhuv7B8JEbWgvLf/seoUQG1bpzwK9u/gy+cAxw7/3z8fDDc7H4l1nMeT6HO39XwM7T+5XgB46J0HQRRYPIW6PR3tEJWHX4VgI7S8RFjHJvHc7WeVRbvXDcbkUKsErtuA7iZjds2QEuxsHzAD8uK1Km6HYqjCmWiZg4egoQ15EQW41uWIQ3GRk4hDVFQGdnHaVaS2lMgUGw2cdlB6HJFvYBGFEnCu0NxbeXag1MMl1YpLNlQ5FjaBeeldjw7HEIGizrlWG4Y+Dm63Ct0bj1D226fFk0aiu2obuH/B/tSJwqEtX3JKRuPDJeiJqqtExCSLCrDXT1CExnE3yfnXZZWFnybRRR3eQi2xkgNtvQqLPY06NAlRAteLmMNlHlc8Qk15HN5FGPmuiIttNiVMDumtBANWCb8zYwvaLO5aC0E5LKWAQ1FvwAO9cFJ8/NfyqC+pYggCUku5iZTTFGhGKBmPE88l6H3jvJ+mN3I0iuWzNa2OtT0/DHp6Zh9UANxx13C+779XRYGMDaFW/hyps+DQdVVOsR8rmmNkhUW2uRMQYRBRRnMBTP3qhnYMnOqFeGOCSQ+hgg2Bp22AMELCIPa9uyH/gw2a1JyKdLDhWBOAEG/QZcrw2O8HdGw7PzqEiIMz4veGxWiM4pV+LRp67D7MdX4e6730HW/SaGtZchp00otheh1hxSmN6Ynrzir8WuIbHGoTzoIYMsfnpZHtP3BiqNNRhlbIGebiodsUuZyKkBJMkYNCMHAakDzDEK1ErYuCZFmOQSr7PvoYBJkyM88OyWmDMbOPfi3+KFuTW88O6WWPXatrjjkSEYRhcsjEbDb2l3ZKNeRLaYQcZphyHdGB6qIJPpgW2NQmSwgY6NRXVUq5PgYgtkja3Q16pBzPX4pDwe2xkuhN2ahBMmndh6J8HPbxyHer8Py5uBKWMOwlYTiQCt4ehD98fz87JYsRz42pcuxt33WXj84c9iYE0/TvtRm3L7EGwRSwjHYDE6RY2NuJF/sxH/7/9HXUV6CYRPbMYgE57n6+QQexixuQFifwzYFcRsVGjZKHYQtmLgvnsX4LwfPYwrrj8Ep5+fh8Q2OjrpnUfaufXOR4tx+vnfxVln9eDUr2+LKy8LsHB1Faa9XAH8JO8n/eHKj4s4/aufwPYmob+0XJVUmkEXkIwHkjGK9GA1WLkHdF8nloZovhhJ0AHHGq2dYTHJdZpZEAZ0+Oe68fkjgROOBU44roZjjh7EUZ8v4ZjjBVOmNVQ+6h8OKEH0fAqSnotuNQmf2Fqxau0K7L/vuXhv2WzceN9WeGfFV/D175yOBQvHYc36T7R9x5IOBLGtfNFi23jvw2U49wf/CsuajHpYgu97sC3CCLlRevCcsQhYnVe+DBqQYaTqJjn4rSGY9jA8t4kMDXhswG6rI0YGr/xlC9x0aYhYuvHAg8vwyP1L4MQ7IWy0tJ25GX6AXHETwngYrWYDGWsMYHfB9OpIUILYCYhtJjyufUwupdwhPaLtIyBUlpFFzI69LVXMwPA2KjqyNjSEuOHh2cdLOPp4G9vs3MDlF1+DOU+tQ0ROBKOohEWs0qccFSYa5SJ++MNr4NoO3l+wDOeeeSdiUpDGWW3mIbdKxvEQNxJ0OAZiycKFq4v90T/Nw/1/nAVfAtz8xxvx2lNvIbFDlHuBfLwVVq4fi+9+8232e+LFucO48VLixXvQkE8QmdwSuxGhBqdAxrt29JXr+N4ps2DZe6ElDsr1KgpOHj8442a8t3QxvKyLs868C8ccfR6ixENitxBbyxFmViFBJyaiDRf//EWc9pM/YObBBSxZ+SnMevozaHd3xnPP1pGJLZhJGzryFGMgi18WtSorEGNx+tc+1HGvGMDXvrIcMTeLRg3SqiqpkpOh6CLZJmsQcnOwEURxkOy2Y0OKiUeffQuP/G4xip6Bu+65B08++zocswdhE3CqDn47y8bO23wLb7/3AxxzZILd99+I2HohJZRKfLBb2PY60IibEDOLnuxU9A8vR5y0wc0WiWhGszoaATrwh99/gkMPr+Kk47pxz69n4dYbVsL1O+DbNThWBldd/yfMmfs8ujPAz65+GO8u70eMIcSBhSAw4WXZbwDMeuYZHHn0XnBH/QlzXvoc3lp+APbZ80d4erYDumGB9QnESvD6extw7bV/Qa6YwUOPvoaZn/oG3nxzKYodRZTJzZ5045gTHtU+8mUf9+Pnly9HhN1AbU1CTImz90DhkhxCy0LdaYCtWWuXlbDkw1/h5JMyKHQtxLkXnIAPFr8DaRRw7refx08vuBDHntSPt1d/F7955iw0/QMx54UMuD36pBAOybmyiRpVKUSQ+Op/aEj+r79AhiwaXeJ5U4OkKHdSTJJm0dgEz9glJcwxbbTCOjyS0TQKePCuEi75SRVXXXkRjv+OSZJFNWi1ahYH7jSMUW2jsHxwHNZtPATbnxmhvqaG1WuGcch+9+HFl47G1tNiFLIWHnihhhMPeQvlhbvimKO/jEuu6UCAXlAIwAi6eVQ1Curm64ZC+CDFcvOQxEt5FbSlM+V5cJUzO/12TOpIw1eJrIDpHJK5WB4skOw//4/BeRwUOu1s8yYAVKMOmus07Hht4Qos+PEueOzRAswwixVrVgPeX5DPHgVLpqFWm43Qn4wDt2nAdlxEjQpKG2YqbKtodaGtEGv3W61FzgFLCWZMexk0j2Buo51iLomfOLokGiA3iEFiolDb5nsmD+CBJyfhG0c/gluu/ip+/1gT6/ttrB9ejl/esQsOPrIbzWoDrtmNkGmffCdgtCMWQa0fsMbmkDW2hR82VXzYM/fClz7dj+KY2bj+nuNgsauTeHNh9x83RweuOR6mT04X4OmnvoN9t1qH2U/tiT23SeBXhrDDnj34/BdnwNPrjhT7G2OZtlPXG1lEoYvH79gT8/6yBFHJQV8lwTfPWY6vn+7AslbAtLNQciN7nHJotKIS9jnQwi/vXI2zTn8WV592Cu4738LCvoPRqF2JR57+F2y9Uwce+uvncdQhj+EP930W775rYmjTYlQ3rYNr5uH6PcoEWU+WoOCdgqP28mAZFiRuYvXHDY0sar7gzAtszFszB4/eE+Obn56O8aN9vPL+ERg95YYU35/p1Q68Zx6Zjn2fXodf/XwSBtdlMH+uiU9NXY7xo7dRvpYoGIceczil1Y1NJOZS+OymSybh5gf3xwmf/T3mzTdw1K7bYFNpEMvWlrHDnjb+9b4zdHPLFKm5GCGop8RHji2IJFJOkJjNwqWq4QAAIABJREFUHezMND00hjvw8+9VcOdVHVi6/DCE8gQeeGIPjJ28gD4m2o2DsGEt8JUDt0LQ7IVP+oD4AjjN0So+TAbAZrBIDS2MJqrNJrL2FjBMctQQnpvgkhu7sGH4Zbz4jIdPTfuMOjifVF7BX1+8SGl+D/p0N86//Blc8bMSzv/Scbh81ELMW7MYYXU3tNs9gLUWLsPPRrdCrSUo4J1Zh+K7Lx2AradzbEZh0epP4Hc+i2zmK+hxR+HePw7jq8f+GbdedgieuL+J3r690V9+DytWDGDvmVPx6J/PxucOfwTPP7k9jts1Qm95LD4aHMAxx+Zwwlem4LabcjDicciQtU9KKBhFTMib+POrdRz7mcdxy48/jxfu8bF4zSCOPvFw7PWp7WDlapi7+E6sfOtizJzajUJnD2oNAoffQZDthYOjlaeIBtNzXOXN8dwRE/0fi1v/z/8dJ4r0IARtc9GMyI9ES2EbJZC/arWaRZM4ZuV+QIIolAfurMhEc5H0WKtlWn6x7LntAtl60kOy57QfyTYTzpaJmaZ0YqOQdfobJ7LwGEi9EcrJn98gDs6Td98dkjgRaQSxLN0wJJb7R8liUM75Ns9Rk6GGL5WkJvUgkUaL5w4lSYj8YOGD1eZeLXTwuoKG4r8UWhGz4Cehwgv9hL/IuxrUe4jj9RKHQ1oAiZMR9MY/GFD+dqSQj3/7Igs6BIFU/Io88XxdPAxIDolM9ESyeEGuv3WFDPq+RKEvHy0fkkn5ldKJldKGNfLlI1fLQ3ezGr9MfndfVc47/wmFAr3yskgrrMnKTwaky3tV9t7lVb1n1k6vuOxNaXMvlXtv+Uir5mEkUqqXxfd9CZM+qck6+e0f+6QDS2UiRIpYKFdcs078mHC3+eI3hmXrSdfLdtvcJnViBqUqrVaKlgpa6TDGjUTWlyqy+86LJIeVMm3i7fL+cmrPl+VbR5Ulata02k5E43e+VJJOM5SXXq9LIP0yd1EsXfn1ilY5eA9f6k0RDi/P06iLNPxEJO6Vhrwuk0aJjO0U+eszoYzCShmHD+XLX35H+hLe2iJJmivlmM+tFNt+QzasEClVhhViE/uBVMKW3HJrWUajJmOMTeKYH8qjT2yQZtTQomtFRObMLUu3wXsQOfPLsfz6hoVScG6Se27RCrb0tgZlmykD0o6mjLGrsufWvjz9dF1sPCrf/OpciaQs1aAqp3+hXzqNDTIeLdmmvSnvLRJptKriR1V5+LEhaTOWSAGL5NbLRaplkeP2XStd6BMPNSk6vuzYMySVDZEMDcZSr0USJX3Sit6WZrBaYqlK/7DIHlu9I20IZAJC2WHqElkxFEiJRXspyyWX3Cm5/F0y624+bCKaCPPjwLKY3S8vvtMUGIF891s1+ZcrN+nYk4vvoQfq0koCCeVl2SB/kedntaSIshTsprhYqCiTW68oy9TCIr3/Hq8pC5Ytl1a0XubNG5ZR1h1ywgGvSxARsleSalyRZiAyWA7k8APekyzWSbshMm/VBi1AD/l9ksT9EskmueJHLWlj5cF4SB77y8fy2f36pM16R3orZSkPrxWpx8J5Vmo05JpLRTqVW09kUk7ENhbLI09H0ghCqVb7uYLliYeXSg4vySiTpK598surG+JzSUebxJdN8ta8SMZkP5IJKEknhuSQAzbIUFJVRNr5Zw1LEcMy+4VYYhlIq8IjZuLZ2RuliJbYaMl3Tx5UNE25XJV6NZG+Sll23Wq9XlsRm6SAihy230rZyM/9QGF55QpBCA2pV2uSEDIZifzTSZnUMRxJfzC85f8qtzO5ClTFgR1pIRKStrtMaQxo11iS9KDZMhE4CfJxJyqyHJYdI1+ZCMNzUHc8bTQM3SraEgOGE8EhLD5wUG85kKyFrAHUypvwwbujcegRb2OPz7h49sld4GTWw5dxMAILVqYGI8nC1c7EEAiZr65r+zmVrKlTSAc6jKrK1sZ7oNq5pZRabNOlR95SXgrNI40UBhODjGRkB/rvXwpnZ++/kRYV2XVGxjMmi5inSgITrcSCQbVpn8XPPHyx4LJ9c1hQGFVDf5Pdih5qvoseCtzKICrJGIyVCiKrhcAoYLiyEmMKWygfdN+gjVxXHUa0DCw+mtnpaNQyaHOofJ5onh/eRsRxH7LRbiiVm7BGx6j5AfLxaLScCtDswDgTqIYxgng54mIO9eokTGrj892EOBmDbIbt3VU45kdAMhktazRqcRGlPqBn7DJte4qSbdDWqqoGYFDNpW3y+bHorQFjC4NIfNKFrkKl2YaCNQl2WIZjfwQjnqHhuVUM0V9djFHZiag2xmIaqVOdEKsGErTIvxELzCJ5TgTdPrsAN2Gj62Ko1oEd8j7q9kbkW13wpQRxJ8OOQzTCMirOJkTWMDowDVmrG436MNrywGCtG4brI9PIAnnme7swFJHEMYDTrMAaW0cQbwErJOse0PSG0WWQ8J883IJ2x4UZ2jCSECvqFrItB8XxJbhJB9iTFqGClh2hFmaR+EOwvAy6nD4EwSY0sR2aDp3MvHJ1uxnWCxK0BrrR2UnxnwFYEQtYG+DketFqjEGY2wiUx6CzOElF6YKoCZdyck4Fg1ETW2AC4CwDjE4kyQT4dRPZoo8573o4cO8Ep3/dwO33VNCMPNQDC0axTzsIs1UTRpGappNQpXylSTrPMXCzw6pzWanHul5azQRj25ehUWqgPb8bys1+5DLr4Lg5tDAFVj2DZgLYxQSBT4KvTSha7dqkwhbryBV4foBBx0ZbqwtNCVBzTYyOBA3bQTUCuiguUgWawXsodIxHYjsYqndquq/lDcDjuDg2Cl4nspJDpZpliUCj3ZKVoO434WRayCODItV1htvBBmOfZYggQd0wVJzCIO+63YF27IRmCQhy2iiLbG4AGaa/Ghtht/Wj5nvwvKmoN0102D5cySEcBpzOPsSGhZrfA2QSzZFnnQhJvQ254hq0anUUcmO1uS3ARuSyXUhAit208f2/tyT/t5+qkeZBlJlHI3siLOgbMjwmOXkYprSNZLnzI0cH2LACFHJ55Zt2nABeZMIhK1hXBkFA0qEhbS3OWdQmr6qahWN3aBqcyso8LVtGe7b7LLKlS2FgIqaPHwKyJTRRhot2wCzCjSzESQkxiWasHBIabMnCMLu1m4kt++QNIIsbkxFkM6YIKY0zyVtcJTdKqRUVvaHENQIjsRBHCciPzdd/xeXB97XxUYmeCFhIuzXZXsuTJpEPh4dmnq6VRT5P4icLThwhCGvIdndoi3RXwYHfsFHMNwG7H2Y4Ht3k5/AC5RnOGFWMHzVBW11JttPdXUYSNwFzEsRx4Iipbekk4I+iGJ7VUm7wrDseJCvoGtWGZsPHKCsLLxej1XIRSANN04VTsJE3O5WzOOe20BIX+bhbC1hRTGpNdrGNRmh62vLMKsXoUQ48coqw9Vq1IMi9kIfD9u2oiLgh6LFiJGEWofbodiFjRci6IQzbgB/m4Lm9sPwJ8OMhtBWnskKHIil+w09ghd3KN80ClhfaKJGUT5qqhk7B4zYrg1y7r63WWeRQwzCy2bHK9NagUERB0G5Q1IKofAetVg2F7ChtY2/n5lRK4HXGKYeHXUaP2Q54LsIiKVNDuKjDtAsQW5Cxy2j6LXi2B8vNIYhS5jdyqkzORDCEghOZlE9FHRcLtFx5ox+5Qif8SNAKumA7XXB9Cx1GDyLCkz0K8VaRpfRXRtCs8bg9MPMluBaJ+tvheTFs5v1zJqK4ATPOIG9llZaUwgijXM7NMqKgE7brIWqm/LxK0ZUAuWQlzJCOwlZIzDI6CyxOjkOYDEKKLSQyWjmy8+6Q5rnZfJ/EHSqh124lcNwWulg0TXLItbVr0TnnjkJslFRyjO32knW1clWvmPBIMZLLo0WK2KhD044dbhk+PBQDtqXHyLmkkY1RDn1kXFsdEwkjJF6CfGaqil8QsdXuRfBDB27iwPS6FaTAZcVb9FQyKkEgPtpsA/mMIFTJG3YCu5D2BuLYQcaisg/Q6UaIkljb1bnJ+iyoFzKgUFBnIQJMXmsDuXwefmDAS0i0THpXC0ZkI0gJAhE1CzCzvciTZtjogJdvwQo6EedqSmvb1s6xo9ZqhKxD2TnmW9O63v9Cjlrt1L8zVmpEDUP5PQjlpyEmDwK1tjJWAUGQgIxhfgzk24dQr1dQyHXBJHF7k2Qm6WAb3hAMGQs3IUlMBNtI+TbIh0G3lIN8xEFHYWjNWkzdMsHtdx+TSgNhK1SrLa4tGI6R8n7wekaoTWk+Dfo3wnZych6QjoG+L1vR+R+p18hlTbUJ/uSLEvVUrWCBjigGS73kFOnyb2PwX/2fcnqMjAePyeqipex+hiIP6Kl7No9dgxhDygnNKjS5jyUZjyRuU5JxFlrJbGaaJVh2j1KFhqS5VEkjG6Z4motPSHzD4plBpXZGCKSMSTfLKGRRiQQ0efg1G1a+isTvQRJmEMknaMQbtOCbdabqQmnS3heJmvDhmxwD8oNndfHZGXqRnUiMcbCVwtXVIqlhklyJG2UA1yPCg8+LbRBazIDppNzlJjkZQvJQj1XOaWAIraiJ0J8EK5uFnR+E6Y9Hq8VF3gTbfHc95CXkSNoTHwg7Yb68qffpGBlY3EnJ2MZnpaK4zIe3w+P1cI8kcsc24FndSOIiYp+LRHQhU9y2Xo/QViTnNcmTlLsRVpgoSxpZ5VzyOlsWiF7hrk46WtJbmhKrMAChVzQWm/lY0rngg5JhCek77UBZ5QwWcnl8RlQtUgOwviMwYlFCOLLv2ZYFx7QRJutguaO0wEfOErLcMeojXwdpTb1CIyVmIhNhxPmaQMIg/ZkAVXMYErTBE3qBtJScLUOQZCN22Pl9jN9yKgxMQI5cMRHQrA8jny/CRgcajTiVACNBQxwoHJGSaRk3T/pKJYsiuZKXy8OybP198j9nHI4fuX1qypHB6NSyWD8h9ztJzWJIQrhbhCQilzbXRIiAPMdGBlnHRqQMdwMpYmuE/11rPAaJxUzlTiGnT0xuD8OFy7mpEFxGrHx6NIguxFgFI+mCIW1K9WqwjhOSYraCIC7DcdsRM2KXLLJWu67zmH1AEiJXYF6/BBsuEtaYtM7DI2tsna41CoPQsJPpUGMRCloQdUR7kkFlwEAQZpEv5tHWYcAl0V9Iigqy/pGVc5xOk3966mOzEfqbgUrd0r/9kxV/1x1KvdWQaRCSext6Y4nBsNkaCauyiIMMLCdBFJpwcv0qHhsHW5JQC2FIyF4DWSXuaUOsdJwkASBJdQpR8luxKnIYVglOxob4BU1riFC7jyvYQxKTcSs1aJy0NMTU1EtU9Zq8yAXluKaKS8oDwsXBaIEFSBbFaKj5oNLjiNJn/X971JsHYjPPh0L1yFpFE5Ekqm6u8lg0rXacqosYTUQR4UA2DLsIU9oRcWzsBKGxUX+X2nEk4wmIyyVREL8r+XTyKN9wpJ4ceYy5ADm5bZcLo6KkRhLlEPkO7Hyi9JER0QFOLyLlLu5EzuMEEphJFkZSVuXxChzkXAtW5MBMQiTuIGxKNEWuavSR7pmVcsMahmu0Kak6AmJmabwMxAYNvQEz5sIwYZNYHsMafVEL0HQZxbDomNdNODH74THkkXaYPLjL/j0TWSuB0dqEjNMDy+pHKcohCQtoV/WbBHHGRYtFoFodnluAYZGQh3zJKSSPNN+s6zK7RepKSniFQQy/LigU82g26E224GSacA3C8siMR7q+IVUMQTgGiDKgNqOZT5S8h/elCiF6jzRKRPeQkaekhdwk6IRpsaCcKvtEMoQoNJChIAT5xKhr3KK4Mvm+fdguxddqoJoknzVJ/VV0w6iBghk0LKQAJniDs0nnF3MxJCaz6CH6WohsmIMoYFrK8iYRmtUynHwJjl1AszIGHjOBFhS+SMUZ0ncyGg78OBWjIBNgNKAFa9MiXLNbz80olJhiKgLlsiYsy0PQogCAgCkbCm40mB7JeHBITRznNaqAQ9ECcrq3w/dbcM0+ODapbHnOAKZDpkoLrZg9CwRm9nDyKI8GU6kEAcSSIKT2ZsjUJsUJ0o2AHj93OqGnJ3RcHLSwQdWlksTVzYIbBimFSTvr2CTUYgol5c3XDSCOdb3wmOSaj2UVHDufijmoB84NKNHNPCYHi9+C41AX04BEhLsynbNG4XyItoUZGqg1+mBbLRSKzKV4ivpwrbw6kRRP4et/1aPWM27+a8R7VZL/kcsg+TZXPPOmNL6JHSulYbNahckctBdDLIZqRVjUpBNXJXaI/fUyod4s1VhI0ONYXbAsF0OtQbhMcVguGs0+dHU5aPqrYETtmkuLfCrKOEryne6IzPqpQ6ReFsm+aYATqcE2mEXkTsLdmSTqNOSU5eIvhCnHrbLojXy++V7/2588W2qg/t3X2JwaAfkM+Yeruui4+JQ/mdm1oAOx78LNN1UWzLEjpSw1ku6R6+VK4Zi4en2i3mmKbrGo90hYIOkfQwuuR27whjILctEngaubJaHCcUgmwAoMu093eMcYq/y+zJn7YQlZqw1UcWnWqnCKOVT9YXR5LPGWRoQMiOOmAgejExonpqU48ZnzEMRBO8QhKqKRqoGY5GymBngCm0KvTcpoDcB0MwrfI80sDSnFYs1kFEzTV1KyViOCF3nIqk5sBQXPRgsfwYwt2CONMZxyceAjMqtwMzaMHGklBawnUEYtm+0EVQeIwCHQjvSgFAagKo7jlGG3N5TD2LOslCXR6IDi1FgvYNqKKazEA5Sqkxz71Ink+yYSiZUcXzd3VSQxtIEKfG6SLnoasZhyboapDHQk81crS+PIuIOkYUy1CQnE+FNgJeM0F07DSGfCZOTF9Bu1BWP65fRuOcMSdXh0IyAtL7mxad6aYSpSG8UoFBNNUZA3OwmayJK0MKkg8dtSgV80QQ1ROi5cc4zMwoBUxSTtJ7a6TV0U2kE4FZVPIzsgnyf5w11uIIy1wiLimBt8oOIUJkn3GRXzftU7TnWXEtKNWoSr5WAmZIkkB3RFJdJIDeqZXUjoiCSU7uAzSH0mGlvTtuEov7qvDg+Flik9ZzGXblKWzURMatVwPOAOwbSqMI02Jfc33AospmPjsSpY4rieEpmFSRWJQUbJHCyW99W3I7k/o1OG3Y5qVzKqJOc2n5naBkbjJhvSyMqZkg7ESaBppVjWwMv3wckSS9+ptRwnatdnSt5tO9+nZuGfbqj/U26Wq4UhrkLSBEnCEDKXymQR3M5wzxyjEy2T97jxwrbGwMuw0z4tR3qZHIKAAPWOVK7IZVGF6goMMSmVy4fpwZSiNmeoRxDU0NnVhqbfRNabkj5YrqsmS5AkA2ciiclopi3oNWj2WNMYzLVQrYEGh8dnMwqvPwpjcJLpS+8pDXniOIbFhUl+ZFVh+Xcm+H/0Dz0cc5GaEuE5SaVKSsaqpnmY/7IsRiBUk0gNMK+Ji5xQSMppRRxbbiDUfaM4g94eFw6Lthwrfp88w1xZtNP0zkbECHgrLkB6bTfHjaeoi4nqIanQgAnHHIWEDRJmiHxHjCZCZOlVaFmX3gO93Y3qRZvoUFEHw6I8FBsEBpWtjkIPiZ1XKayQquQa9FOVhuNvwshy42TymUaBiilUZiEJKSMEesouEq+mwrSJVYCEXYiZLiiyo43prCGYYpAhVum6Gfrblq+CtOJY4OKzaKjJ4R2kSjKK8XcHFA9t2mz0YY6MnYYRbEYLtFJGC80K4Ga5UPlseL1kGcmo+8vnxnDXYoerUvqSGZINGemS41uaDkhc/d00iGIKiBsrB5+wQ1KzErJIYwRNpVGr02YzFvPadBZG4jcGdYadwkd1HUQ0HhmtUVAXk2kdesOc30zB0BTScHR6o3Uux2TB55hTEcKyNe2YJRkfoYwUj7bI4c28fQFx0tTCvklmOjHgEY+t80drt3pOy+JzSxSWx/QP1YnStZJWyuksZDxb84qcjylXKlVqmBJIi/Ae4bMscmrTh5sKZJjsVk41SSkA7GiefSSTwHIph06dQCrQMM3BLZ9zllGaXqamXfTfLBLS92LaxeDGQY+W0VQLUG86hGtYsKiAYWT1WZK50GZ+gn0PlGExmZrK6ibNw/OuKWvGpAufO4uYlsnxCTXlQg1E2+hWSC+fmW1PhqgK1ABCbibU2+RU4mNQdYsRVkyCnNLL/1/6m/knWoiRcGzzhKF+Yigr4dCFxXjNV+vOrJ5KrDpvfHDM+Sbaj8hJytCqprshVZwdg5pu6UPmju23Cog9GxnSGCdUkUgUt6t83CrDFcCMU2FNbteciHzInMb0/nST0VxjXYUDQuauQgee16ZhWr0xjHyOYZmdNlJYaS4yCNg4w5ygg3wHics5T0YMuv7r3/+1OT2kP/Vpp9+NQnbLEbFAxQyOC1mU++En9O47YBkFsOFFlHQ87SeKsDE1AK221GBaLdhOrIVb0lDSCJDSleEcm0BsO0i17kxO0vSeLW3tNiGhj0AAz2MulJsoFzuxwfwuMeamris3V4WfmLjoF2+jUvGRbRWx7745fOFrM2DEm1RSLYlGwbAFJjm1xULiDGju1Iu71OtgPpdqJwaFcjnNg6xuDizASWSo6CsXkOeRR5vKOuTmpqqKoJwMocj0akKSyC2QJZ49Ike0C8seQJNporgAV1w4rCF47Gb00fITxFYTeZtk/B0IylRkAaxsADP/MUKKLGA8HJmgY0NxVYeLTvO4vTr+QoL5JFIHg/PaNFxNKekTNkZ0BTV3zzlICs/02ermqkYllW+inBbnoFIv8ysUpoip4PJvc4Ur9e9nEb3ov6Xf+DU1UOFI3YQbAAV6U8FYChjrhqF5Um4WqbNhRNwxeFTO/RG9QDqHYN3ITlOF/K5JSgA2yDCvy4iI/Ow0pGkPgPYdaB3i75BOerEjqUDm50ccD70L/SyNJtloo8aZ+4lBnUseY3M0weI+vzxC1K+WlNfH/ygfxkHUCx5xtHg+Ghe+T2pVtdF6neng0VFkDnzEEbB4bkfnsp6Sp1KLyOiFZVVach6LDmX60kvndQi9ZkYJDK1T/1Mda3rUI9fHT9MXrzY18KlOI6OekbHSuhavgy+GKn/nP4/8+t+9M/K9f+qP1HNLvUQWx/g4OFHZJmnDtnpUtqjVyuK1lwdx7+3sLiRnxVr0rutHIVdAMygjm+tBh30Sxm6ZwU+v4i5PL4bJfE60tGBCr9fK0KOK0KzZ6c7rUKOPngY9RLaMb/ZA0pumF6chDMNMNdwMVfgEPCREo7Al2aCuSEvzjnSsNd1F/yemX862ahYpuItmUq2e/8F4/jsjzsU2svUzZ0mC/IgcDPSyyOdhjoZF40aECvPpXFCU/eEClBoisx82xsO2PfVsYnYtJgy7GRoyWmAxNkCosl3UcXM1xcQogoUbTkbm321y9MUtGJ4FX5gbppHm5kjuDwqTkog+Qi5jo1oLcf3Va3DzNeNg2gsRRvOwZqWDL548HnHIvD/TCTwyN4w6Ynp49CTNNiRWOW2y8S3YCQn16WXF8OnlkCi/ybRDAs+jgkm6gFsN5g9dWG5Rvd6sMxlhi0WzBuBECBO2Q5cQW52Iw4IqzjOwYQQqoaVRWmJxk8rAMkfBl0FYcRUJxWydos4Voia4UbruWPqRmgKisYM1oBJcQWCjo91DHJEbYiTlQUNk0/vkouNcZ0TEoJxeNRcpjdWI4fibAeY9pU1hfO4m0xYjqUA+chUOGAFMcSqpaRtZvPp5+lhHZllqtNhMMmJt1FhzlRmq58mv0btleiRVbqPQLw1d6mXT8vMYWm7V8TS4gad+4t90GRnGM8znMdIX10sa/fEKEzbgxGmOX9NBvBx+l8aaa14NE+cDn7GOkn68+Wjp8fnFCFHMtcRj8zq5KNMNJrV/HDd+tvluaRZ5Mgriph2/OhR8i3/4UsTZSDewvpcKBKTXxTQdv8rGNc51XrSthULaEdoNPQTfV1UXkrunAAZ+lJ6Ca1GTqPr76XvphqTH5htKscTogY1yujvB+Ds1dPV+uNGkBlLP+b9sqDkK6aj+zTiNeNf0ILjjUCGZ/BrjJnRjYEMdr8x7Fhf+Yjd89rMHoVLpRaFNsH6diyvPbWHMxAgXXcdCSprP5QNlyKXjywGxWvCMGgKjGznCXIy2NL+sngsLSMydUUWcISsr9emw8gC0a3zRAzViVnUZpY0UM0m+ROKWXBYW1RgIb2PBh4tPB5yFRlsVjzdPofRo//3fqcfDE6cnVySLx3CukKpscSFTwdlKdQsbNUFXmwvYDI+ZNGArMHO/hPHxrTpsm6ovzMMyR0jUBY/Oz9mJ6CFk+swiDrymJFUulbQjUyXrDZf6eD4ilGFarPRPUkVo0470ekhYz/0obxZxzx1zUXBOw/3Pbwsv+RhdmSwioUfDDbRLJbKYgkkserMVxPEU9YZItE6ZM4npwXPib4Y6MpHSQhuVag0TfshaAL2SHFpxW7potMhGDQ0LWSXJKaLG3GqGUhv8bgiTkM2RVA/nBesgRtKuCB2G36HBjs0Anm3AbLMgrbQnlM+x4HYjRLt6eZSfYs6XquB5OwfXJYcLC7LU36PH6dLBSsef+TpN8XFvpEPA58mTszaQ5lFTk8s0HWMm3ggfGI0601J8hxs253Bq6NMHSg+On/PFha6xn35fDYp6j7YuMX5Dv0ciUh5nxLNM36d3yGPx/FyPpjqgdJr41ThkOzoNMSPYVAWJcEPeQ+oU0DiNGOqRc6pB5ne5cTDToE4ujStTa0xZcY2kqZrUy2WNJDV86V3wQMykc1tLDRujHkNTh7yqVLxXL5cnURwMRXxHPGiuaaYtNu8e3MVoA/UUvNf0GrjWeZ50DGlzUvgbzdLmlEs61izm2zDoAKrJooPGJ5NiOtLv83dCXVt003RsuUPph+lmle57/M30ergODabDeAEW6wxpDUE3F36Hw8AD6XWTYyQ9Lnekf/KLrXzazpeeh6147OZjd2LsCzv44jCRRCkPSVubenceAAAgAElEQVRfliAqa2ceieOP+/QSmb7ln2XJmpHD+DxaSd58Z5P86fFFsvMeIj+7mIfeoJ1tP7xwk+y0+1xZMC+QxCf/5XqlAA3ZIaddhOy8GqEV/fvrSt/9T3/r9cUiUSJSCXulKU2pkmbVb8lnDpgve+46JPvuHcgeu4SyzwyRmTvXZN9d3pFZTy5XWlftyPwvhAH+dqKRoWEnIsnw0/ZNMk1HSh1JMYSdd/pY9tld5DN7iey183KZ/VSi91oOOD6BHL5Pv9KDNqUkrbgpv/1dJLvtITLrCZGbb3tEZuz6qCzcOCAnHSEyc3oox5/wJsll5d2X6rLjzktl7319ufM+dkhqQ5SSz9dlpQy3lkutVtPWwhdnxzJzx345aLdQttvxPXnsqYpIuErK8rKc8ZWy5IyKwFwtB35qgZx22gPSkiGRsC7S4vMk93hTzvjekzJzl9fkwxUNpbhcvVJkpxnL5Pzv90scr5NIPpFA1kitPCDNZl2paU88Yp18Zs9EDtgxkL33SuQ732tKLBWpBCSIjeUX14psv7PIp/doyL47vytVdtlFgTR4fmnJi08Ny4wZZdl5xsey74xl8txf2FgaisRNOezgIdl/v0g+XFuRGTs9Ixec8boqWHCqXH+lyG67x7L37pvk5fd4JHLvstX1/7D3HeByldXa764ze9opOUlOKklIIMEgvQiE3kUEFDt6rViwomJDESl6QQEF6aLYxYYgUgSlSSeEFkJIryc5deru63/eteeEgAre64X/+f97hifMnDNzZvZ8+9vrW99ab2nIoidrsvPuy2W7XXlMInvvvE6uvYLqmNTxVnKnzmEJ1c0hm+cJJWjJgmy0X7FlBow9GBuBlx2BVylQb3UcDNTKiwwkTXyJUoqek9aatOmrdQnizeLLkAw3+6W/EcrBu98nvd2flyUrN7ZprtQeXigLn1ihYK2cdav87Fc3yKXfXisO7hQP98lfb90oYcpgOcTrVhJSjttBkTrU/LflF1sd3t8/zI6t1sguQGr7NiSSvqrIpPzjYmBQ2PoCWuJazMsTcc2H5JJrH5WqjMjLBeokIp0+UFqqrmEU2dYwLfLk0kGZMOEqcRGIjfWq3ZwvJNLZsUT+dj9jer9sU9koM0vZwjbSzCioV1zK4wnlmitETvkCHVNukJ6OjardWzYCgX2p7LTLL2RO1/VSwRqxUJfOwh/kht8szWjcMiL1eK3UG3RFCeXx5xoyZ+Kw0vVJWzHMp6U48Wy54dZ7pJpulL1mLaHHuBQKG8XCb+TQBVer+nhIFr5skFZyuy5abz32T+Lhl7L7nGdlWd8G2X03OpA8LW88YJlIOiRBvEoS2SiRLNfzc+IJ10kFT0ovmmJjk5gIpIDFcsqXvqxz6KIrfyJF70Ypm03pxDqZZCySnWZ9WkY2VaXlizyxKJCO7qViY7VUQLODR8Wr3Ch3PfyMNMM1Mn0yiyuBzJvyuFRwnxy7300SS02+c9VN0m3fJjkMKC1/5pT7ZNHKmgyMJLJhvS+7zr9MXKwWz1wrHQiliLpMLa+Wv97ZElLmU0qrp/0ScY6RAtyWJ8hkE1oqqfD382zsN2Mj8M9HINt7aNL+Sv3vxR/BGly2xWOx3jaoYOcpXCYO2fUrwkh6tLlT8sbBMR3cdPfe2H2H1+OA7Zfh2WWCuG4g19gZs+Z04Vd/XojAuh6LFo9HXzOF463Ftb+dir0XFJAYHmKsA5x1MMgxNVkPJ7oj68qwYfjyN6IgWNMmQ2wQngv4rRAd5Squv2dHPLVMsOi5FpatyePBpwM8vizE3x7cDe9+8y5Ag9utl74pnErHg9s97pp4TNmYDQ3WsGkgj6OO7sPS/olYNtCJt779dsTRb+EblHYl02kYqTWsyA87ZXOHBAq6XffDyIdo1sfrFj3x/oKfP7IOd60j+P8oLFn4Wux6SBVLRjpxzoWbMBz9Dhs2r1WMKSHJ3JKxUbpisYX9dnwAK/qW4egT1uM538KpXxuHqO+9ePsJz+KhexP84YHtUJl+GUoTfom1A8fgyh/T39KAM3EIrWoOCGcg8GNc9csjscdOM7F8aYTFi4t45JGDscfht+KH18/KYG1GC82woXR8cpbWPLUf6vYU/HKhi8c3jcedj21AUngLohEPN/zGwqkfOhhBazmu+PV6PNOahPHzLkZqbYDpWVjxNHDQnjdgpF7Fu94d4Rl/HN51coxW08BxxzyMVc+UERkDCNCETDgHz4zsjZ/edhQuv0TwpQ/sh4+ddgAW9pfw2LoeGPZrcODrfoNW2MRQf4RHn3wKu+1fx6L1U/DosI1TvjaAgdat2DB4N8zcUohLbfS+Nkln9Pxz65vhp7WEwG15tv8efcHY/dgI/PMR+Ocx/JV4ZjRrZlZRzcocQZr5Jo5adiW0fxLx6R/GMgdFkZK6LF8lsvM2q2TXnT4vzRHav4j4FOMRXy669lkBfiyO/Sf59vfXqOBLGo7IsCyUZvywhOkSSVVkiYpB3NhqneVfzKijTLyIfxnVxY/UJESGqyLbT7xKbPNcyef/U+zyKVKc8BnJFS8S27paLv/50zIotZfNqHV3Qd87CbaIVjHTp1DNosUD0tFDL8HfiWudI18799eyaU1NaGixOWJmn8iMif0ytcJMNFDrLDqd/OjqSGBukit/1JAPffwG8YpnyvV3bJKqRLJsmKCuRHbbnm4gPB8i3/rPRwR4h1z7w0U6JnS2oX+eX41l/eJNUnFulb33ukloLdWUYd3/fOw9dAi5Sn77xxUqnjNt5tnS3fsFFmxUWCaiP4lPsSS+5aCEQSrVmshwIDJz3LOSx5Oy007rZJMewiatCDALrUcUzOmXejwk73gD0d4NMfIXyw7bf16eXR/LapoAicgZ5/uC3Gb53sX8FoGwyMXPrFcTGa6FsuiBqji0uzrmGRnxU1kvGzRLP+6o29XZZMkjNdlheirFygYZlEEZokRQNCA/+YWI57bEMS6WzvEfkrL7N7GwQkzvq7JuYEjWrRuRbWZ+VizrBoH5WXnfmV+TZ/pE5yfLRS15XKryoFopcWBY0mNGTcGxlKm2ih69EtfW2Hv+/zwCL053/3lEf4WeISCetFumhFHIJkwCm1wS1tDZhU8jDDXrePNhD8BvLsWf7zwNZjkAeQQ5S4WM0VGcCMc+EVHcQFfvEGzThV8vIInnQ/xtISE1pymWX2lDYthDZRMigwm+5Fcjg8mwEcYjMO2NcOylMMwnUSj4mDY9xW4z5mLvbedjn9k7YffpO2CnyTOw07RpmJg3UCIK4WVuGZ2bzT02NkdfHMJ2BDNmdeOOpw/GjrsPIl88Bl//4n6YNW0lLv/RIHxivYkpNnIwk8lIsAyR/QhgLkVgZVq6BObnyhvQavShp8zMmgiROgxKBNHFhR3SNIRExDWMVxIQSTbNeAiO1YBTGITR7EQrugfdU5Yin0+BpgWP1P6uRYCzCF7BRrM1jLw5CRVrF0QNj5BdtFqkzhqIwhGEzQ5YVgw73wRd0OPcI+o8EjR6kTM3wW9lOFwD3chRthJscg7hij8A7zjxN5g7xcPQ2oMxd7Lg2MN8LO/fAC+XAMFqOIWmNo2CJs9mDNczUSk5sIwQkbUGdIwpOwY86dYmjZsf1EZddSCCGRLA1puRONjWs01FFbXCAWy7zV7YYd5ueM2Bm7HbfrfhoKOfQpR2ontyBXfe/WXsukMZvRM+i5999YvYaeJCXH1hP5rNJtVLkMNOMGW6ikM93/xip42X2//1S250ko3d/z80Aq8y6oOTdHTPx61gqogEiyD8xFXIFbvlQVCFmbOUWjqwNo/DXn8hgngu/vzIe9DVEaDFAMgLFDX4zU48+tBKTJsyEU5+Ih57wETwxhg5BhASFBwzQxVof7uip0YDYhsv/bLnirEsFTg2O8R5BD4RB4S+hfjlH9+J7p6CBiUv3+4km1C7+MQZgR/VXtaKix1yFWnXe5Y+0gyZwDDsx1i6fAi/u/l9WPV0jAvPjfHc8i6c8bXr8Nr95uP4/bfTkhHDjYlZsM0lGBwqYtlTWRlFEkFslgF0wg/rak0VG2UEqLfhoYQatJDPlfS7+fVYqa611oiSU0jgpoIYWYCtxlwsX2Vi8rQC+uoRFi8l6sFA7LdQ9noRBiOI4gzWGIYximVXq0yOWUZo1RVd4rgVPLJQ4BqzsPMeMYZH1uPJhVOxzy66hqDpN2C6xJEWUHLzuO+BBKd85iTssnsLbzj2HuzgG7j7rw/g618ZxJH7H6D998XPrkK1Ng+FMrDwkQT+UIB9X+ci8rkomxg2p2Dlqha2mVnD8lVFjCT7QHApSuMDhAaJSQYsm7TfAipWC2n0DFxMxhvetC2+cP7uqKCKh5YUsWnweIzvAUb6EyxelsOP/nQAVj/t4/Lv5PDcc9vgwguewfZ7uDjp7dOQBoE64YiZMfUyhEd7pj0PFxgF273sFBx7wdgIvAqBOoPaPD/UGYCUlVjFQFpDmkGR8skJnbGeiMEvYNWqTfj4e29BOX4L7lu2o174hKu4KOFXv3oMpszGY3cFuOTSu3D2xbOB/iPxzTMSJMNrcc63JqBS9mCZQ0qtFRVS4kJRahcHeQTZsTx/bP/gkRnDpEC9MBPtRdrKg65LFJvpzJc0KHvlGJF5n6rtxf72cOw5yHlD8H1DUUX/4F2f/5WWyQmsdzSjI9SPWVcQJli9cgPets892GlKCd+/agI+c9pyXP79eViy5AiU/R6EDaAxSI2AJq7+HsX4DTz8pwIu/+GIvr9LTYeI2hoBzCSGFZVQJuTcStEKhhFEAtspA1ZOs0yFhCUGirkJiJIwYwCWTRx92DG445Yd8abDBvGl85bgp1f04I5bevGa+XMwZ7arFki22YlWTLlVwCkEaPl1eAG9AgdUOwLGeNx+91M46cDxCK0Itz89EzO63ou37PUD/OpGA/scTOEpehc2tYUgMhEfOqyBZbUQl10V4+TP7Io1q/6GB/6yCM3VczFrYjemVLpxyTfXotW3GEcdPgfvf8+TiMMWNqw8EL2zGtjv8Abu+O18vH99H049YzUuOH0e/vpIBbvstSO87g7kSpQZpYdCF2JrHYK4gZ3n9mD76S1c8u1NWL3BwYePr+DoE5/G3D1XYY9f7YJgaByOOeBHmDLuw7jy2jze9/X7cf21Faxe7qkkboqCYrGZSbPWT/KQ3hRnvGXL1A7S3NGNZdjPXwxjj/7pCLzydR0tUj7/MQpteB6eF6c1hS9lcD1fEoqXJyL33TMku+5wqhj4onzgHbfJOWf9Ub5z4Z3ytdN/K9/60kNSxCcF+K4UcLVMmnqB/PHWxXLrLSLbzrlfYL5L7r3vCUWNpDIgYbJcwmR19jk8HB6DFk+fP6x/+og1RXbuQx6XLymL535VwtqApPVE6v2BJM1BkXiZiGwSaQ6JVEXC+kaRgPVaGifoB/7Dj0hi1syb6kCsr+L/UrpCiyxdFchbTrhegMvExO8F9sUC81LZ78jHZbFq/EfylS+wJfoD8fAxsfF+2bZygxx98BpB/ib58Y/XyHs/ep4Ah8tj99JlPZCV/XSGvk7mb/tn/V5BInL6GXeLYZ4gV37vZh6KVnvrUZ8ECd2dI3X5fvPxRJ18T8r2xwU4Xfbc/zZZtjKDFI4MiHi5z0mucLr0bxKJfSqo91OfXlQ1XRap+P787b8tBr4jJ73rSVkxvEk+8JEfioNr5MD5d2sxtxmz/t1PTI1CBb99zhpxcYcY+Jk4uELgniY9pWvk15cvVRTPX2/eLJNm3S6OeaV04XwBzpNPfOp2aa59VpE0a4ZEjj/qCYFxvhjWl8TBhXLggvu1nhyIL3nnZrFwtzSkLpsS4j/71YH6L3dukJ1n3S6wrhEDF8ibDl0mPlGKaVXqm0Xed9wN4uGngtw3Bc4HBDhLDtr1EVn8hP65+GmqvRVJCcXjhCMklI8VI9qeB4ke4z+cFGO/HBuBF43Aq5BRM2MgU4ulDpJOyCshkDujUFqkpeqNzAsKk4iqmPkjZRy45wfxzjcbqNYGsH5ZHZZbQ+CbKOarOOP0D8KyBdXGRiw4cHscfMg0BfFfdVmIe+/dDb0941Hxxuk7N2o5FApUF38+g1ZwvOo/v1xGQzID0PIDFDzqXzSVXWfnEoRYjWJhmjKoqL4VRSRYUF+BAiwTVb6yLUfd/o5/f2e0zQUslRYi85FMKUvVzGZNtXHF1a/D/Ll/ggRTYBqvURH9t79/PLaZGSOIbZx5bgS3tA6d7rEY3OzjdQu6MHt7B3v+oB/ztythUucRmN3Tg3xXDYnpojNfxOc/twnz549HK4rRbNo4/LAKTHMvzNulV2UL/GAzijkSODxEkqJQCHD51SXs/Jo5iOtHInWW4qSTt8PM6YBP0TjHx4XnHI1mi8w61qBXwIy7VWcksFfAoqGDZeGrZ78Wjz32DL7y1dcAzhpc8v0jMMF9Grvu0Qm/JcgVWmhFI0icAqrRcnzkixUdj6HB8XBkByTJbth93yIOe2OX6m4fcPh4/OSK7fHnv2yAa8xGoTiEz35hMprBWtUf7+0ALrh0PGZfMA+FwnYI8Rg+9OEebDO5AX+kiO98s4DNwy2IH2J8fjCj6kXdOHCfXlx8dRM33zIIqhF+7nPTVUWw3rBR7AEu//UxmH3OTxCGE9FlHomavxZvettUzJ0HBHVKt7qIkkRV4qhjk/VCmE1nxCSy3jLuHzVE/n5OjP1mbARePAKvvMxpe3enpTlWQTgxDfKPSONiMKDnYAFUEUuxEUEcIm9Pg5HSbThzieBBU1fZMhykGdUItuoakK7pKAMwiSN1AaduAbV73HwOg0ObUCx1giJJ+XymfEeJQ6pr8ZbRT1/uSqH6FhAEARwyAqnXwGhEEwMq/WjDkApi1A8gA9fTGjbFpniIZpvO/+KBH/15i+aDNjezssfoc6qtQdZYW1OBlZpIJVkttAiHtvLqhO1xASHTnaym9lrEMXCovJdaql9BBiIZ2H5cRyFfUmW+JACaFKvqYjmIFFlb3yOOGkquIgcrpkCN6SuMMvABz0sRRaR1OwiStQjDHhRymWgVyzc134eb3wQrmgLbSBCHBQRYi2JhvFL2fTJPXWBwaDk6OrrhSieCmOJVIwio22I4cHLdaAZsRrookVHKGGdQyramiwIDP6WJJeVrSSPPppWkocqi0pQ1ispwinkVtSPUj3/P0aVYVhwOZELyFOwxybjMo1FNUcjxhdQiNlWt0aLRbFJSxmm1uVJr+VQvpOxms2aikMm4IA4j2BQYSgDW5518poFB3RAzR+1hnpQ2A1HZbfxCejRjpY/RyT52/5Ij8IoH6jYbNDuIdomOwiukwlJDw1SxcMB06hBs1OAcBxPg2tRciNAMcpo580/FzGQVHdV49pUanAR1lAu9BC+oFgfV3WzXU/lFy+VFF2pgJbVUqbnERLcbOllW/ZLjo8JPDPQMYrzgYgrF2y4CSqnmLCRJU2U0VcRJqPTHbJoaDVQGBOz8y0Rq/fis+ZftOrKgQpEbupJwmCynrKJSptVERN2NhBq+FfhR9tl0D4lCLiQkK8cIQx92jnT8BBJYKFYoPGWh1uCuhE7NJMJygcssxcKwmY0xqfihg2K+iCAgF7gBO0+7pRFEoYucPVG1e6mFwIUpMTYjtVZAopmKhbecDtSCKkreKqTBDJi+ATdfQgLuQkg5ppJZgpY/grwXwHVKmT4LF7uUcq2kGBfh00pN8kp9tqzhTDfFCGCrcqAgpGATG6hWhCgZD6Hkp91EDjb8YRsFz6XhC6Ul4Dh1tFIfUWrBc0qwKGhlDcL3W8jnpyCVAuo1B6WiiYDCXdTpEIFjJQiSzcgZ2yGmG46zXpUFY9bdE8DLU8qVbGRi5W3EPhvihorAUbI1E8mL2loOxec1zrVW3aY9awB/uR3dy83Psef/N4zAKx+odRRHRWqoHcBsuqoKeOTS28n4jBrPZMbk1r/t7EAbJwZGSgE6/DtDLbp4MdCFgw4MtMGiOIplZkLu1MHQvbtByFgVrktpRDpeGJpVU+g70xf4L1wcukK0p4KRaYowGfLjGA4Fm01KrLqIA2okMKOjFrZkWglMYdsZ7ktPpn8UqJtI0VBvPaJMkHaoHkkrrMOwR0AlwYiCNQzeoQMr7VEUhOU0VKCKGX2SiI5NGFJvgrqJIVw3j5CKafyN1UTQZBPThqfZJG22bFUhJBolNQchMpQJ2qMDYb1HF1auWVahhTT2kabMOB0YmhGXEaIBH4+h250OKy5mAihpUaVCfToLeSNq20RXDAZk1+iAaVOcpqaBl9oXklDlL7Nuog4Wgx4lWi2bC0uo7h9EdDB7tQsdaLRieB7JUrSbotNIAzY1OpGDBF2oh8MolKkgEcHlgsFdmTDgh3C9IuqNFrxiBT4DLXdkrRSuuxa2tQpJY0Em6ZlbjSBIkXO21cyZOszUQhGjpUHYpusLk4G27gxF7K08kxFqyFDv4oU7ty3gjxf++qWnydiz/2tH4FWoUb/M2GqAzl5DWceMLcgaLQVXTIhNphozRVPrpQr9FQN+q4qEW+6Y3osD6gRjo0uDpmE1NKsSFBEyglJkieJA6oP3XwjSWx164AuI+U4TCrADNuENrD6mARzTUBsjQsvIyNMyRCSgLKZJh91/40YxGarZRawUJSUUCiW1s6KbR87y4IcpPNdAEhmqpxwmVHOjpc842E4TadJE3utCGtO+qAA/GqQmjfYLIj+HvF3RLJBlFqrqWW1dTcMOECeDausVRTnkHCry8UykqDcGkU8rsIwu2DTnDIdg5ChaZMLzijDTqTCZJdOMIBoPGHyOYj2JqsNRpikKi8jbBUQh/R+p6+1ALB43M2UaF4RqdupQC5jCgdRDTuuIY5ZZKHrvwjFzSKUJ1+GOwoBn0xWjDirCcTdD1EV9uI5xE3NoYRCuwisFwjErmHDzJoJmPzx3ogpRNX2W12y4pgFbd2Ah4jDOdKhtOqfkQXlonbKEa1rUCXcRp77uFiLfVahl3uVuykLasrXlAJPCWmTFcoGgI0x7Tmj57r83H/+NKTX2p/8PjsArn1Fvjc77B6UPMGuUQuZKgWqmYBXR6J1iY0Pwuc1V/d9MCosXKLfucViHl7PgywbNkHLmFKSBAz9gHbcPxVIFoe/B0Aus7cnGCyQ1tqpRt4WuXubEUWfYb9WRd7XViYSegqoOxhp5t2r3xhEzTFbMKYJfVC84vu2oKcxLf8Q/yqiz0odhcKEqg0axNESlPGijlnleWrkIUcAsOURYLyHv2bDyK1Rr2kpnIEpbCBoxYrRQLJHw44FvEcsIbMPNLJN8ZquE6UUa4PgZUUiR8xYcer3FPQiCBPSJy7lFdVchJT+Mq7CSbWAkBZgeLagCBFJCwEy1XkHRGUDeXYbmyHQUC70am5hcNlt1XYDzbiGrPVtrNSBzYUlSyl7Sw9JEEiZwcw6CdBNYznIdBjcuunn1W6RVEiVHE2bklGpNPa0CI6JTB51VbIhDq7L1SMw8RqouPLuMInWr3WH6vSFJu1WKmX0P1vBZ8g+xBkm8CQWzC1Y8AQmd6nM5iPOkyl8GjSm6S8rlE/ghVQA7VHxe4pz2EixnBJa1Rks4Es5UgwHYQzAMGj6wtNMDUqG44LG/wcdjt7EReLkReMUzaiWXMOjyOmOgVg3qUa1XipBTjzin28mUGUZKYomvOr5xXEXOIs6XOsYJbEqHJqmWGZAQsOsgNX2YNDEF3cgDOMUALqMRZVOpYazC7nT1aIucaxaTNRKzwWlnN/9kpFhLp+sMeRjUbKYnIqUVY8pucgGJHA1othMgtVhY8dpNfnb2GFjY6fvnt6yZyKxqVK4xy7C4IGWeMkWEkYVmk84mAcolF3nKdzM/sywYOROOXUbC+m8cw0jobEG5UBOx76HkuQgM1pmzWq/6UZodqLeG4Gn2SssiMiEtRIENhnVuCaj9nCR0UHGQ85i50g8SaFLyme/NMaE/JZt8iJXjuaYvgl1pKloESRdcr4x8Ma+1cpYJWHpy8qoZqcfvx1UUHGo4Z4ajrGELDQ2EJQUiJOgD6ekioTVzFFRn2DRbsK22sD25MmENhtuHOBAUjGlIfIFTYDU8QjOuot4KMa5zprpmcQqmIb+PqaQl7tXIaDTMnO7ibM4bqwWDcyouwcrF2rBtRalawdFdKE5bCNmJpYuQOwCDzuCJo01SWoOFSawGGIYdQdKaWo9lNTDuuLa+5P6lulh2ssf+/796BLaeNa/IQGQohKz8oPtAvU55kRGex6BU2RLEVa/WZt0Z2Lzaw8qlk9DRTfcMNoMC9UGLQlO3+PRDK5dzmLrjHNVsJfU5wZDa75jGOFQbdZSKdIsootFswCzaunVXxAbXDNp6aUx86UDNvIcXtY0yJDCQhAxiFpJmCjtnot4K1PbJ9jKnjohBIKLvE6nwrFm/dKB+ftAZQtoQmfbOg3BGv2Wi5TfR1V3QYFuLHtN6bsGZjDSdrLuDkeFhdFTGIainCIenoFhiI1HgFTM37JxJmJug6ZP8QgEsUa1dy6QzcuY6Tnih45SywBs1YJpFNIbpAh8gjBPEPt2TG3AqzynHM2p1qDAUs9U47sDjD+XwpgNCDCdlOBjGEYc38YtbtofrsJxgAW6EkWAZvFyKWGwEjW1QLla05EDPQBociEW35iZMlnvMBEnaQBSMQ84jIYhFJbqpNJCiHyLdaI5M0d0W5Qby7iDSXAtokj7Ol1cR+CnsYhMVeyIClqxsLgy26ovTlceP6adJUgplDGrwwyHta1jJbNWspnsMIgtRZKDQMQXNJrNitk5y6lrjuEWk1gBCn5rk9BekMWsAF7OBlGW8Phhqs8QeAzNnGjq62XsocuhfnRvPz5KxR/87R+AVD9RZFt1GPjCbZbdFba6yMkKaFlSYW0u7dICw2ERz8MDDAT78jj9gKO3D3KlTUOldjyAdhuPn0Ig78cTicdhntz1x+2X7gVMAACAASURBVEMTkaZNdRLxCnnUgxzyOR+dDFKpgVCaqFSKWiPNcKysXzpqn6UrwsucdwssaeRhuHSydGE6w4BVQa5iIApMVHroEjNO0SqmtRlOvhOR5GA4VUTJZpiSYblHkSYv/jjWi1l3tQlD0+0wyw58RHiiCSc/AjtHm/oAVpyHZ85AYK1Di8p5rQBlrwOVzm6kjQG4BPm2S01OLqJFi7qlhEJ0BVDIm1Q/1Z2IZdWR+gmsXAEI2QKtIbFr2mjMezk0mynKnSkQG6gGQ+i0C+pSnmACYpRRKBiKwhhoVjGuMB7vfMuv0MD+OOHoXmxcb2DHXafCwQCioAjHpdB8QxmPI7IOjpHCK7G5N4RgqAvF8S1uTdSD0nJKiM0+iJWg1ZqHXIn0+Cbgd9HYGy2zEwaRJY6LMk+xx51FoKrxljkBdQlRKrC5W4FTpK/lNOScbvVWiM08fOThcdMgfbDyvTCjQfjxZrheh9b1RY/DRBi1UK6wi+kAhRqStI4C/flUQNZEwcsDVQvwZgD5ABLZiDhXiJahEZ+YSOjtGDlqOpzh4zn1mb1nlx2ThSxLefGsGPt5bAReOAKvfI36hZ+XlT9GM0aGJrMKE5V2gGHmzfqej2bUhTtvX4eTTlyIUz7xNnz5bAcSO4owIMb2bUfcjrV9I/jBr/bDzKm96OwEYnMYSdqJ+/5axeCaGt54/BTE3oBShNmRty1GMdr30AWDhYXnHR5efJhbfm4fqxir2ogRIlAShFGK3/84gVduwTK7tbOf0v4p6ETUMrDrPgm2mUMY3EsH6i2fw7GI6ThOGj0vaFp/qScpbrx5pcq/esYwnNIQ5u01FV1dXSjAwx9/uxCFUjcOPdzG4JCPctc4rF1i4aEnVuKgfbdF/9B6LN+0EvscMA+P3xGhVc1DCutx8BE7ora5gbvuuxeGvyNeu0sHZs1hAKFtVoLq8Fp0dgla9dlgol3zW/jLH++HHeyLvnATFhwVYdsJM9WV+S/X+3jffzyHpvMsfvizvbHt7A7MmlFALl0OyxyHuNWDRQ/VsWJtPw5781TEWIuSW4AdlZXMdN/dz2FcZQfsud8EXWeCZABOnu45Bu68YxWSfhcSTMOg9yh6Z47H63Ybr83TnBNj4TNNrH86gp12Y3NzGd757p1hSw0Sx0gcB0MyhBt+sxa9uUmotlZg90NnYnb3dNZL8MfbN6JZjXD8W+fgpj88jN6OHbD9DgY6xnlY+Szw6MKlGGoN4U0nzUNRSnDsAc2M6608brppAJ5bVzRRPd6AQ456Dcq5TtjE6ecfgWPSQNnTZndmEcLyTjsuZ9Wt9sK85Yetp8LY47EReOEIvIip+Ar8SApt+0Z69CijvH2fSkNSWpuovQl5uhuk0VwrlNpMEpFf/HKTlIvflc9+7B6Vi6SQPX1gNq1vSA6nyiGH/FxZz/U4lc3VUOhycuiC30kHfiRLlqyTIFmvZgPxqLIp/7rN6KZg/8vdUpoc6FdoSitaK614vQSySZpJUxycKwa+IQa+IjBPFdM5RYBzJGedL9++7F6pq+TnS1PIoyg7Bh4TTQT4YTw8Purrj+S00xZJzjxTLPyndJqfFuCd8rHPPCwrBykVK1LC96SCS0TkCUnlaY6MfP/8p8Qzfyg/vGZAPv6ZCwQ4Qb513pCMcy4TC1+VSulc+cWvRL74+dsF5jFi4Hw56W0Py+OLWyrjqm44ZDs3atKUqtSTUD7+ySfFNE6WDlwiwGly/EkPyHBDVF50ZuVm8bBSujoflTyukZ12/kZGivfXSCib9TiP2etmyeP7snhFVaqJSJNmDlKXJxcNSg7vkbcd9y0JAlGJWp6qRixyy40iucpPJYfviYczxME5MnPmF+XOO5aqUu2zT4jstf/d4uICyeMHksf35OIL10q1nwdfE3owfPCzpJB/VAx8TeUIPvyJx2Tj5lAp63n8WiqFO+Xs89aIa7xTdt/hVKV5L1sSyp7b/0w8fFFc53Q5+RN3CH1l0qgu9aE+OfNrD4trf11snCeW81EBviDvee/jsmR5W51AVkvgr2nP9bpI3BChNIIaZrzcjBt7fmwE/n4ErDPOOOOMF4bu/+mftqoBjz4cvWcpkbhWdhpNeumx9siSQick9UCY8tz5Rey4l48vfvwWrF9Vw8HHOepTSDLHksVNrFu2B2bMzmPb7Sym1Ljzzudw489r2He/7fD2D0yE5wRa21S3c01eCJHKMhtDG2cvXf3RrNukh6AL26wgSsoQs4jUZEllJg45ZAoOPWIa9j9sHA4+YjyOPmJ3vG6vOXj963dAR9lVFTqO6D8rfdAHkLAtDoHicLMXaza9ePEqvPd912P+zj046+wF2GPv6diw7ETc+qfZWLB/DvNmV3HtxQvQ6e2Bj572DETRBBOwZNFk/PbG6XjDW7qxuboSj971Ptx3Wy+OPXEyDjx6PO7/60H4/XWb8fC99+MLX/4S0vqO+MttazF77jjsubcN0yQSJwc/aqDW7MDZpz+Byy50sP20ffDW9+VgxofhnpunYMkzT2Cvg8vIGRUsun8KktTBf7xvBg44YAb22LcXdlxDbHSoX6qX68UT9/Riw/DT2P+oDoAID9/DN78ygpHVO+AjX6lghx0nIGZzmTVt08A7DkiweWA+Tj0jh0MOm46pk3fCw38bwrJnq3jtvq/BSe9YiUcfFOy/oIST3nE47rtnHf5889343GkHoNEawqmf/h1+9L1DsP3Uw/Cej05AsOIE3PVnD6vW34v9F4zH9T/bFRsHp+Pu25bgEx9/I9749g4gmYOPvPcBJF6KT5+zI/bd/RBc/t0h1O0leM1Oc7FuPXDSW+7CdnP2xVfOPAK77mcgWP9+PHjbFMx5DbDTbi1ISjz2xAySZ2xEajSy3Rvn3xYA9Vgm/T8daf5/fr+XjlL/4988QxRkFHI2zojyKMOgQaURQ1Ky+hzk8wVtaoV0ho7yOPaQBbj1np3x1iMeQqP1NM7/aQRn3Bycde7bsNv8hfjjTybg0KMaKHoVXPOdYTyz/B789PqzUMgNbiG7tONhZuL+X7hYqMXBUBqGPqycAGzGCeAnRXWmTqNZsBxiNFj3DBFHLsKoD6ERIslRipU60C99I8abwVodk9svZa+JRBQQm+zshc2DM7D/kQGOOHECbrkJmLu7j8TIIySbXbG9nVr2UT9yj2VowUhSxWBjGLH9GN7z8U6c/p+TYdqTcfF5w5g+PcW5Fx2N444bhwmT1+K+jz4I056tLLtmvB6mNOGUbdQW13DhRb/FjClH4ZsX74xDj52Bhx7I4fRPPYjrbrwcH3zuLHz2zGm48gc3snaD8y8/DgYmotqowis2VGKVULQ3nlTB9y9ehKu//yBO+8ZBGN+dYqgf+OG1T2Kf3WbiuDfNpic16s0WcoUyTEPgenUQc+2ku6C7u4GLflzEzvu3sNO8Obj95hgLHx/EW0908J8XvRZTJrmYveM+2Lx+B+QrwPJnBnD1lQ9ix7mvw0VXzMbO++2IE44WfOqDCX7y05vwuS9MgJS6lIF47iWzcPLJPXAwCVdcshkPLjRw+AmHY3DAhRMBpY4CvvXFc/D+D+6JKE0ROfchCd6C/mELbz72OLzrTcAtN0Q46OgYvt9Ejn0Jj6zaAAUaBHECsf+i5rw8wWNB+qWviLFnXzwCr3yg5iRtZ9C8YGn7PuqAzECdSghLLd9dWCDMzkOUMIMFRmqUvnSQ76nimYUd6G/Mwpx5VUSNOUBHgM7eGt7/oem4+47bccfdAYz6sXj8UQcf/sSBmDKNFDFfm3JEUqmZsX57BkWGBN6UnvLiMXnRzymS2ISbMxGQLm5b9J6GhDbO/sKtaGIBlZpggw0/FyF85JyVKGxTxMlzt89q8i96xxf+SHy0mV3MmnCJCvqYlo2pU3tx2kf3wfe+vy3uf2Addp0f48BjfodTvrA/ejrmoYYmQpPEEgNhsC0oFJXCx3B9GCEbmZiL3u69kcbX4T8+sQCOFWJkhFl+Gb1TErzhuMlw6D4erEeMJYA9AEk74bl0K9+EhtjwrKlKk99pwXocfuyuaMQN7L6XhV32exJ/vd9E3IoUGJl6P4Yp3Qibx8HxYuSLI4iiISQEJxO7bk7D586ehXUfCvGhtz2Gy67bHp/71CWYPnUHfOasidq87ChU0FnMIYxcRFaMr19h45jXV3HWmXkUUcQd9/8ex75xPPbdtwOLF9ZRzAGvPy6PqZOIJ1+LN79jKgwQDVNDlE6CyFzM3WUl9l0wE8PJKrxuwTRs+9oleHAFXdfLGGz1o6OjC+87uYgwXAfHnYJ8p48wmYHfX5fiht+sgUGbOLMbpnUQvKiEWXNcnP7l1+O8M4AzvzyEX377ORx20hqc8qX5mDqB/QkHtrdZce9ebgQSTnqeDZtkAl+j18ML58HYT2Mj8M9H4JUP1FuXOZSZxUYeM4oso7ZIvNAEI+H1DNNsQowhWHYBbmLB6wAu/g5w6pffjTMv/ApO+eTOEAkQtobQO64XR76lisuueAJPLNoXRsvDmk2dOOKEXVHsoBt7T4bLTTXf1VFQzWWDxApGRbarXvoWJdSGIIQwgm11arML0oKdG8B1tx4GMUzVxzD5TgaXgDySZDbm7lRHOXv1S37A3+uNEPNt6l8Wig7OvWRfHP4u4OofrMPPrroejz85Bw/cNBNn/7Afe+yyCSl6USxSqQ5oJYOKNZ/U2wsrtWAlDvr6VgCYgv5VFubNcBBjCK04RVTz4CYES5RQLHTDsjsQk33JHU5SQho34JAoMhwiR+eTxjbqXWJHOWVFt0YWwMAArCiPqOkg3zwWZmm4bZzjIG/nYUd7wjCHdXHbuD7GAYdOQG/PRNx+2zLUh4v4w3XQ73DIkQUkvkmBFFBYyXESJEmA/Q/uwj33lLF+tY83vfWN+PWPdsQdN3Rh3YoA4yYcjEZQRBBOhYiHSEWqaAJAYFEZrUYIG5NRH9lOd1FiEf/sI2+Pg4VtYcXjkc87aNRDBBhGySmwv4gkJhV/PT56mofD3tCJWq2MpgC9PUeiWEhgWUP46teOwZ6HNnHjzX247KxrsfiinXDnjR4u/fl47Lr7ZiW12GzKJuNUmEvnNwEjmlm3E2qdeG2IzkvOkLEnx0bgVd+DcV1wAcnDANl7JUTkhGdhSbGrSi9OqWcRoFz2cOV3q/jUqY/h9PPegVM+uR3qVKRLcui2p8MQF+vXN2BhNn70vR589zvrAUxGw2dGaKJZp1hPAbaVU3KKXjWjubTG1ZcL01RGrSAmjDctolUXJEkVjmHDiss4+LAKDj+khCOPyuGIo0McedQwjjwSOPqIcdh20kQ44csLMo1m01qNMYj4YHkoAYdlw8Z12G3PU/Hw327Fj69cgKeWfgzHn/hu3Ps4dxx5OH4XvI4A9UaorDpGqQceWYbPnno6TIyDZQboLFfg2nWYaYBGdQg5y0Ee41CphBne2E5RbZKN58C1yJqLEQ834ZCubtjId9rIJ/Nw1+2Cc89aj5IX4JdXpPj972OIsRaWvU6JHghcmMEEJQNJ2kQQjgNyBmzpQtysoKeHgkwO1qxdgbIzDe8/YT1STEZMkg7LG06EJOlTJmkYL4Vj9ePzJ9+Mz3ziJzjiTS4eWvV5/PiGUzE86GL1kx5sn/iU8fj6mdfhb48uRr5i4CMfPQcHH/QBNJopimWCMX3cc1cRF32Lpg8eLv3Oc7j15seRhBWEDaobsg5Pyj1Zmx1w3Cpsi3OnA3/8wwXoKD2CNxwJPPTAT/CNs7+B/hEXSxZH2GfelXjob9fj69+YjLtXfBnv/8jJWLZsEtatd2EZE4CI9Q72RihZGAMO1blImGrvLtvTLpNLGAtDYyPwL4zA3/cX/+d/k4nntwX7VRj/+cdxTHxDXdRAQBJpBJslpti/L/Ldc56Rovt9+c6Fz0o9FGm0RIYbg7Jstcjus/vk+KOHZGQkkXPOGxIDVYF1g3z7wqekXm1IM1yjCJBqbbMiSMKI9qciCrJIRKKQRqMv/K6jaJDst4R6RCq2L2FdJKlLEgxKq7ZJEr8uid+Qob7NEicNieNQmvWGJOGISNonob9Gotqw1Pv+RVTJVodBpAtvgZ/IU08uEeATsk1Pv8ycIrLjTqmM7xYxzd/LjXetEF+aMmXaZdKZXyjbzVoqM2culSmTnhPP2CQ2avLdq0U+8ulbxcJn5aGHN0pTGtJXE6ngCTlol4UScuilLhed95AU8CW55vt9inpIZYnE6Xrx09XixyI/+dWz4pq3iG2vlDk7bpQOb0Q8bJTLrhhRJEycJjKrdI3M9m6S+qBIkqwTadaF8I5mjR9SlSh5RmryN3lidSATxz8qDoZlu1mRLO9bJw1fJIz4xUNJg0QSGZEwEjnu0BvFwEKZvW0ou+0RyPTJIg4WyfHvuEY210U+9fFrBeZPZfKEEZk9NZGysUJyzhkyFIoMRyK/+PXj4ppXS8FbI3N3G5COykbJYaFceuVS6UtE/67T2yyDQiPlzSL+cxKPBPLlU+8QB7dIT1cqM7cbEdgXyh/+8rjU0kFZvLJPYJ8sJlbKdq8V2eV1qXR2rJeSfatc/1seO895TSSk4USfxOmIzo9INkosyxXpkrbPsZ7oF83BLb/b+joh7CiNM7QSPTf4Ty8hIoSa+pifG8uQJFue57GEW72WWJoM+qSfP/r++oGj78d7YqpodJAZH29tBs2/039bPnsLlKr9Lrzjl9v631ZPjT38b4/AK1/60Gy5nULwjgk0//F/JtXdKBWaV4ZdmsZaHw3DAn7zwwYu+GofiuVtcOkFS3HNd59AY9iG1zGAjnGdeOq5qTA7x6FYKaJnfKdWnQ2jC5MnVuDlYyRGASIdKJY2wY9WwHOmarWFmhZsXFrGKCvshdtPbk+39Bp5mCbzIisr1zhkvbGhSCowkC9TY4IlEQeO68AkZVio7kbZVoFbaH/vl1gwqWPBG6VUqZNt8P0pOZQzMWPmVFz3h/PxoROWKx67b12AYscmfPfyQ7DPviX4sY+77z8Zr9tmETA0CUND/Tj8xAYOPWI8vvHxFkoET0gTnYUyJCLpJUUUACUngFdI4VjM3ougiFWlowqDOGF4iMniSwVx6ijh+fgTpwHxTHz6PSPY9AQ1NRKcdvoE/McH+xD7ea3hj59SQtCKoXwPM0Rq+bCdIhxrE9KUmtaukmpmTEuRyzdA7E7eqWPKhPGIfJbBQtSHQ3i5DghlZe0qLrrq9Vh24CasW+agumYYSSg4dP9tcM1VrwWkim+d/0YMVyu48Sfk8QQoGD2487GvgV6RTr4fx52wAy65ahy+9KnNWPqIj1Kuhu9csBPe+S5KAQAdOR8VuwSD2Xk+OwbLGMaXzzwI9WYV114l6BsaxJ9u/yT22hMoCrDdNv34858uw0mvD7Dm8QEU7QY818dHT9sbhxzDETZgpCUYnGeKBCde31ZdFfphKhtXeTNU1mPBjGD59gRpyxts2ehyIrKuzXsqRRrkALC/okIzbZRQrN9F29mKHBqdbPw+JJXxRu0+qlZm7SLN7Ec/E+wZ8b+MrUt0UqYumKGjSPLSm0FVLf127doNtwdas8yef1GZT5nH+nlbv6b90rG7//IIvAqEl1Tp2jypClHTCcIJl2Q/sxGW9MO1XESxp2pklkm1OBPNqgOHvai4BdftR87pQRw1lRmXwtVaa6u5CT//YS/O/Eodn/m6j698EXB8D0GUQsqPIY9uRM0pSOIKciVO9gCtlqEqdCyoPr/9HN2Xcgx5kGx88jJKEFP6jW7kWxhlKqWhwTpN/EzAn9Ieqp7ZQBw24NiUXKWd+vP18Zc6O8+bGKSIE+oij66h65BG26gErFgbEflF5HOdQNqfkVOMDoWBNX1XjQFMg0I/ZTVTsO06EhTg+8Mo54nAoMi+qWNhmQ1AhhGlbH72otEsolwAElmZKcfFXQpDTFTjYghhkkMYViivAscEklAJjkp+TNyliJFHGBZRchO0whAFu1uFn1KjhkCeQQ7zsb4vRP9QGW8+7G8QMXH/sgXIm2vgMaAzECSkWJOqHiA1h2CiFz5r5XYRURLAolpeDOScumquJMlyBHY3omQSXCtFimpbIYUwu5x6Q/IMDjcc5CgJHQMlKt5RH1tFpHrUid2z+hGHIQz7AeSwA8SYi9BQ2hXM1IaZJCjyS6c5pBTCywNBMoLEcGA2Cmg0gI5uPs36NBULx8Om0JjFOVQFkk4YhB0ZLLOwLGJlUB1CU1M+DgCFJVKGgImDBwgP2EOa2jqvSK3XkhiDb5olDpyfKoUg1Hbh7KLJRZZECHs9ZHQmPUrQor45kwDDsDNtdcmQVlxMUmq1KPKIa4IDkw40beMLneTMNyhBzH/6C1JCM+lWQ12Snp/ZowF69DeExY7d/v0RGI0G//47vew7ZGgLheaxaaRqRAKJiUKgmlik6Adm2NRctiwDHd0WWnEI16Z5QBmUiwziEVUhc3ItLH50Aw7b/Vz4sgC9lcMxtZcQq4YqnjnIqdVUkDiwkk6tx0qcIIgNFDxKhVJLogWLWs9qJsAvsCXNeMG34QWRStLOOxgQmDknsA0LlsUrhIuRqZlNJiRE7ecs63nBG/2DH1Qz2jJ0UcgWLzq3MMsWRDH1TXpBLYs0pDONZCJBKUWRfKT2erjptkBUQlEzQhtBaCDnNmCrrgRdbWqo5D3ExIHbDpJWiLyXqriVZeVgGxMpXApXoYQF2MKMNsyw54YJ0zURNbvgOrbWopMGEFJi1SO93Idj8/VlxUqX3BKChGp+zCCpuGchoRdZPB6WVcLJb7sU9945EaX8jpg+/yGQ41l0uvTcJMkALMtGGtAUgFKmtLOK4dkRwmYNObusEqMUx5KwBJhV1YnOwYXH5qsESJOKNi4Jd7SsAHGYBeuuYgCfGbddgEQU/CpAwrLi9AsWcfsdyLs+UsNDEIawU1G1PodGtVYDiU4LZrY1mBTAov2YQfEvar80Ma6DUqdEwLhwPQZS6soAQbwBNkWn0NnGF+V1nmRRlfNmdI4wWDNT5ge1lavbJsdZoOTORyOxqjISPcXAy6BIvLukFPDK6Ok04+BcNYiFV4lcxlZee8zm6YrDpn0Kw4z0HKn+CJMok9ek6Lzmzpbvx72AqcBTpcoq9j3bbnLRyV6j32HLvObnbH37x9fT1q8Ye/yvjcCrGKh5QFudSJ080pY3LUDoEMCbmerFwQyplW6GjR6dpGnoAflEnU0sy0MdC1GohNhj18MgpRU44Z0r8O73TkWcrlGYn2HZKNoHoV5vINfRgt8wEbY8FCtZUuC3YuQ8Wk8x6+EH83/tY8gORBcTy0hUtIcuIhpIeW/wIgl0Y0mZU7atKHhPDWKWMoy2pjNdXtpJuL7jP/qf7jLaT6hmNq/Z9s9sNHJLyzWNxybBJBjuZoi1BGY6Bab0AuYGpEYIBONUwlQlQzQz5UXZryJVLAXQJZyqcnlGurShlmFpTG3k7GtTMF9lWChbyizIdrLPpWhRYQgxlsLGTESmjVyR7i5rYSQzIEEIx+pFqxlrgE4ZXCuZiFZQLcItd6Dguop42HEP7oL6Eclv8ee7ToNtVZHUKtpos5iqSwKTUBQjQZRGqkDHnQxdYpjNmSmDZT3bjfs8kTvDwkbAfg56njAfwCAs4TkhWoe1H45nAEddhVoq7BVHA7ALFEmilZkJJ7cGUViBjaNgU+7Uq6tinhEU4Vsp8up1FsFPhpG3y7Ac7lQe0Z2Lbe2BhE7mHkW+yvBrJmy3BRhPIudsQEKBJkJSVbaA53E0ODvZeaasATNsIkR43LwutMwQA3Rk53dmEKYIOjNdLp7MnJVE04TJjDaemYGoLNLU+wDNvrMFgueX85vSr5rtphTkYpBlKYPqfjR9YDFFkLIeJFmGzSzfsNhFp1QjoTSZxC7fTB11yH1Qwljbj4zHqBz5rYPz6EzWrzD2v39jBF6F0kdW++IxZkEp1YlCLLVuxUgc0aJuAwzAvDB9igU5LRisc1KJzqLAEC/WVNXWbLeAhiyBZZaRWIRTZRlFs9ZEJTcCM6Q9E0VyaDBqqYg8KwnNpq8oA9ooaVwgKSWJNLiaJqF6VDx7fqJpvZgWIxq/OWm5RWXtL0NmMMqJmhUw4PMfM+Gs5pwF/xSGZtz/lTPEbCnLbjheRsQLlXXxGBI2YLgcC35Wl2pHxFYNtlNlbqrBgBeVmeYVr64XY7oesHsR+R4cBaFEQFgHbGLWGaRjRNgE2yghZWas7BkqD+UQG8zuDSRmH1JjDWxMgSGTEIQDiEED3Bl6vhRBoaVUE0FUQ7G4WSGLdjpNa9Z2rh9+I498uYSQZyURFGmnlvgImy5cl7rhrNPzm1J1kPh6Fmm8bKiJmkBLhbc4DlEUI0/xJsIkOd7WEBBRMjdFGlVg5Rwtl1HtNY4D3UlEcUPdYUyZmpW//CF4JFbFJcDaCBhlhEGGl48Djmle3XuQTkCcsmYfwnGp3FdCREMAaykcl0UDV0siRDA5FncXLIE1kQSDsPIejcdgpJPbZT7afGU63FvqxAzMzJ55185j9DmTb8T6MXdvtNvhLiWT6s0C+ZA6JekCHk7J3sOmLjjLExR+YqAuI4m6NZgbnLdCb0+aGsQahC1m0UYntHau85oqhjwO7m6rgDUCk4QtBukkj5T3vI5Zq9YgHkJohLDltlUi1v7daA9my0vGHvy3RuBVyqi3Wll1S5cFbQ1mpiCNmZWxucctLGcCg6GFNLHQcJdoEwzxrmrHRCF+2xQ0gy7NXHOhq2YB5XKMMhXN0hFIrh+xEcO1J+PKH9yGal8FE6c4ePu7d4LN7XjAC5gZNbd6rNuNjh0n2pYfslSTEYS2Hlpj1CJ0+2JjUGdwpx0XM19m5tyUMvDzPdqZ0cvInDKL5jGMGu7qqkB1QR4SGZBmS0seVBlEnlKnMcK0oCazghq49WeQpui+wddiXKa+Z/bBCCfAjGYiimtw8sOqa+2yroQt+wAAIABJREFU9ml0qSeikTPgRjYsezLCSJCjOL8RIq4zUI3XOmUYVeEWO2HJRPgsTyCFmyvBCLrVp5EZvi+dKLieBhqWEOp4FkV7LpIqkCtRFS+v9Wq22uLIQs4xEYT9yDtl5IpNdVBJ4xycHOv5FqLY0ODKuUBsvU2VvZA+kb4GHQr5B9EwjKRTF/HY71EpVfrCp7QlYwaZbyEMaccWawnEcVlEttT42HQj5NySBp8kMGG4k9tmtlThM2GbEzRzT9JntaRjE3Jn5hDENeSsnDIALHu6nqAYg3BMLlxVpKGLNMzre1MFkJhw02S5w4aoNjlLSpznLCqEWfYsXD3ZWDf0vOlM1JOfZdBZPS0rjelzW6YogzZNFLhA01hzRBcIgzXpJA/DpiVbAlPYAOSN2UY2rzhbDZY5+F52C9IO0ppds/HJXYgGei4U/DMeH1/Mf3ySx8+EhDslHpt+QPszssdb7xRHnx27/++PwKug9ZHNQx7i8yeUgbhdlwUnc7uLzXCnTs4JvDyRIB7cdIJuWx2zFzltZLkkWyNvFeGYBbBZ5RqsRdaQpv0w7QFYdgWSTMK5X/8LPnPqpdh+2wX4xte/h3k7zMa8+RMRxQlcm0p8Wb1ZM1clv2STeXQ4s8nGxYPZDY+fG0SWIrgdzGfu4LqV5d+163Z8HSe5ZqZU58vcz0ff88X3DO76/fkOWiM0YJq8oDKzVNceZuhCKlVYaQdcw4VtRaqNTSsuAtYYvLm1RVoBhevZwDKJPOCxxolmdrFBjDBLTSwyF2HnDaQWsdQ+EhmB45hI0ipMcxNM1waigpYJLHbOaCtmtZCkdOduImwyU6ORa4RavBn5QhlR4uKyb4/ggVu78eBfYgwNpJj72nGQJHvfnFuBz5q7G6k6IFEfFll/RMhYluLc/YB2V4yyWUat229+L62h1rWMk6YjSDEAxyrBNIjHZ1D2FVNvcqFxqKCYWX7ZThnqOk/5vxYzvwI3ELAoSxoHSuwxvUgXBtOkHRjTeo47M1gDJrcgSSWT5rVasKnxEtkw3VTJMUuf8nDtpSMwrS5MmuzqHCTu3nBMtNIQieHC0R4IpzvnEI1wGWBZ32Kzj0GSsrbcRXEO8R9rzWn795kfo6HzQZ/IVi7FlvDlzMRZomKJh0YW9GsrwqB3KMsQijrh4sdwm5U1DE0C+Pk8Hu7UWsoU1gSJ/Abt13DR57HScJo7L16rWSLCe9EL2crKJu235nFn18tWi8qLJ/vYz//tEXjlM2rqd4xGaM2WmXEG2pHWppzRhGVQL5oMRQ+WkwPZb7fcsA7XXH0bps0ch/pAilZ1BShu73MzGaeYufMqTJ6RwydPPgEiNThSRhiv08w0Tmqw7TIm97i47PwL8Yb3jMeVV3Th8ot/gjcfd64GKd8PEPC65vbSYGbNCJt1xUcvGB1V/poXF11oeIFQZziguzjLeQkS09Jgp1vzZAVMiyzBGVpLTNFsF0Je6vxwq8qGELf+hjYOud3VWqXNC7gEP1gEz54FJk5RPkaQEg2T0e81hzfyWi+3mX4aKfy0AofFam73cyzD0oB3AkSWab0WBW7zPQ36SH3YeRKFDFAwP+Pa1wBzamYomx9CQwQlKSLv1DCMFUhNgWuMQ80aQdEmWE/wxS/diKvOez1ikJE5GfPnRnj9iQESh8zIIfVItFBALP0qWqSfkzZRtfrQkfTASH2Y+QR1DKOAAmx0ogka7bYQYAhOuh42JiAymYuuQ5nBg+4/uoextG5tE+UgEQy3E5GxDi5yiKwZyjlJogYco6jWaKn0I7EHYAr7H46aByAhEYsZZl2DcJ5ZQTIRETbCEZa9KiACnW42OUrm5uu499ESzjgrxWdaLeyz/zZocePl8Pg7YQUuXK7nEeALDS26dGdIlIdpRMr8NByWL/KIpAkrfI2ee5ZKOBkyYac81NE8pPEDMugjHWXSQYCoDcyDOP3wnX61dWbQjrhQWAHsiF6TAXyrrqYXYauKgke25JCaGbD5ytIKlcgF0zTYizrjcHdAW7QycqYHU2I0GdPtjVpmYpkxTCbASTx6JSMssP/BrJ1oGmbc2Y3Nep4bUvXHbv/+CLzygZolA13d2ws7G1Va62LgY5PFUwgbDU2JbAgTH/mCg55JZMwtxQUXPYWT3/9WHP2WSagNR7BKyzAyXMQXPtWH6b2z8KmTK3Dy9K4TOBQVMkPYUkKrkeB9p+ynLuarV3PKTEIky5m+EbKrmVvEeJjLMgDNqkczmq3q1KznsSwRYwWjJCxrHOyigWotQbFkwtT6YR5UtWdtlDf+eRwKLMNRhN5LnSZilm0mONziZ4YsiFko1AyFW+Y68rm9ddoLM2lEcBLWSJuoiY8OowchEymyDx3Cu2iK4KARpyg6ntYsU+TQjFrodLZF0AKYRSpKjJpYpoNmMBc5Gg24KaJqEcWSAbgbYTG7irrgsaFljwBxJzqsPZDmGxCngSTphW03EMVF/PIqH6Xup3DlH7aDjLio5Fn/d5EYs2CZNXWWSazNsDAeJrfb4um0KKEXSDwgLcANrUxzn0ARNvWSFoS4Onhw6UpeLyEpGMiZnUDkoabGCR4MWrKxg+CX4ORjRHQTN7dXDGFsjYAD7JYSRH6I1OiHbcxEs9aFSnkTWpgAg/0Q0MWHn1uCUwgQRD4cJ0TILBwTEcbEhqfIc7cR0oG+Gx0FqKZId2dOZ444D+piUhvsRLkrVIOyMACcEndifG8PeStCmqyHaU1CElSouZUZ9LpEiaRw2TzRI6mqGXErGkbFbaHVjGAVKqiH41C2xgHWIJjzusKyTwlJVEeaJEjyZET2wzanQMAyTQSffQPPRSIGTKMX9XoTxSLLXykapsW+sZZ4ctxppOPQZNXOyZMfCytpaNLDiewkWX2dqpaxFSO06erOczfam2GJhIta1B7NUa7CS10BY8/9KyPwKjQT23UtztT2jQ/pjaddaOJddf+aPekHVeQ8bgFL2LgxxDvfsgSNvon4zT0T0NNDVnIKP1qPu/9QQf/mEVx97ZM44g3/h733DrOrLNu3z9V231MyM8mkB0ILLSC9iSIqoIAoXRQQVEAQARsoKBaKNBVBFBQEQRELqEjvvYYAIb1nkkwvu61+f8f97ETUF9vv9fWP72AfRzLJnpm9117lWc9z39d1XlP53Be3xXFqXPHNe7n7rhe54edns8lmXSRuxHu3Xcfg8EpufbDMrGk7EPlCsU3BHGrZrjSbiZa7Ic9OAUl/Lroh0mjS/eg1c4Z6ZSKFskPVdzjqgN/h5TNmQItCyBSraLZe2Eg566x3cfAHp5km2cbP/dZf39w/ZheZZbA2ghwCH1auG+TUY54mop9sOUDcN/jat45mt51mm9ndZz70DGONIrfetxPauqrVUh5/to+LLniOC77xfhaveJTrf34Ht99+A5d+8V5G1reSnbKI6284haULV3LyJ/5EwdmNo0/ahOOP79IUQMLRBDc3gpXtpz6wFS3j4LVFSzjt5N9SZh+Gg/u55AdHsNduW5sVw+nH93Drr57Ha4nYca+UbWa1cMm3DzLCrkYCWScyzUV94oyzvscLz62hO3MstoRststzXPadz5Cxm/FgP7zmRf74hychbOPKHx3M1d+dw9qRl7n5+s+y9NVRTv/CD3jvQZvz1XOORTKaMhPz1W/cwjPPvMSPrvkO06eX+eBHziQc3ZoWmcJwWOGUz+zGh4/OkXUm8J1Lnuaue27l13ddzWc/cSOW18r1vzwSDW9ZsPhVPnf8H7GjLr5y0Z68871dVKIW7CBHsaS6ap9D9r6e7vy2bLHnOvZ635EcuLfw9QssvvL1IWJriandZv3dOO6j5xM4GX79y/MJ7VFemrOOL37mZ5z66XdzzPGthNUZWHYnXsahkYRksxkOOeirREmEG+1CHA9z4qcO4sjjJmuqm7lx1TNw3lcv4PUHp2A7WbZ+Z4PvXXQKw5Ua7WUteWW58trX+M2tD9HqTiAWuPPRk83MumdNzLFHforjPnYAu88+lAvPvpfPfmFXDjisg8gu84nTzqO2ajdTTfnZ7w8xJSAv6qfgdHHn/c9yydefZlJ5LwbHXsdq7eWPD51JXm+PqSqHdGLh42gd23SF9caktW4juXnr0/7tZ/+tPfB/P6PeMEs1xUQz79GlvjZSmt3p5vKzWU6wnIhszkPLEpZTZXx3jtvu2Y6PfPBr7LZDnQceupQZ02zy1hTe95HFvPBslZdPbWHuS4txOxfQlhzO9y/YikF+xFBlOVOTLt6761zmLv4NLy++kE2mWfijUOrQ6K684XYYVYao5EgHZ51dN9nQG/eilRaoV1JcT9UEIdl8xgyaDQZ58Ml7cNnKuAptikSM4dgFXDvihBOGsJQcx/iNL/V3vjbJeTqj1qVjomoHqynJe/21pXzoyAepr+3UQD4a7lz8cFdWvz6b6++APXfv49mHu/ATxygktCxTLMKixb28+PRerF4znlfmVZn76JEcvU+ORYsiYmsRoWzB4tefoNY3n6UrSka58cZr8+ho2YnDDsvjlvpxbHUybobdDouXwpHv72LBqndhe7diR7tx7KEWN/12De/es8xjD6w2TTo/LPPwnX309TyF9e2DjHon1mZxGuBmPE766B/4053d1MMu5sWvm/3oPHkM+Tjmwu9a/Pjni7ngKw6NitZNF3P8MYfw8hybbNElkWZI7gtPT2DG1CJO1sXXyZvt8NoLHg/fP8pYvcFR+xV4+PFDoXg7ce01w/FY8FqVvrXv5pPnRMx5yeWFx9/Hgbv20NOrK6/7yViHsXh+yrH7hvT0d5LQz4kfeZLv3vx+PnTIeK0oMDQIH9jveRYtSajVX2Hcy23ccJMaXDrI57Xi3oU/Vjb5jjrNeOYhj9Gx3fnowYs45wo4/gNLWLfu3SzavQ073pVcqZ96o484LVMPLT4wG15deCxVHsCih7bclpz5yvUIh3D0se9AMhXOPkG45Wfvx/LuI4kzPP/sSRoIz8U/yyJWkUsvHuPKb3QT+J/A4mUiquy/XS+/enYC/dUlzH1me/rm7UC5sIiF6+/msFWb4CYzOPGjt/Kne9sYG5mDw2Q+dvAibv/DpuSdtdx/Z4nTj9qUIVnI89EfydoFCq0Ou2/xPR5/7CxaJ9QRuw8YAasA6USz2tFJh5XZoA38O2f+20//G3vg/9l8/v/0i8oG8CWViiQyLLEMSCrDf4EGqEqcDP+ZT+CnVQnTRPoaPbLP7nfK1PIdsmJZRdKkXwJ5XcbihXLFt2PJcpOcd9l98vWvpdLNqPzm1iEJklj22/4lmWAvlXnzReqByOBAKGGQSmWsIbHyJWqRKAMkThqSpIGYNJe//VwbsAW1Sl0qY75JXhlOVsuYDMn85SIvz6/LC68Oy9z5kbyydFBeWliRp56ry8jIiEg6729f7S3/b9JdDOQjktjsnybP4YXnlovtfV/23ed+WbIukYWDQ3L4R1QQlciDT/dKNX1Otp+wWrpzz4sJZKmOGO7Dj38iUrZErv2pyKfP/77AM1J23pA77krkidfHpFxaIy5zZJ89b5U1fSIXnDsqLfbN8uMfLZdIRpqsikRkpBLL6qH1MnPq+aLirH0PXiOLBqvyubN6JMcqKbV+UZ57ca7MXySy3fT50t36pLy6TOTVpcMS6OdIRKqBptWEoiiPg97zlMD35faHB2Xuel/++PiwSSA85hCR+554SbKtl4nDfLn4kjWyqj+U2dvPlXZ7pXR0/lr6KyJPPDgsLdbtctLHHhFJA6nU9bUjOe7ouQIXysI1dZmcWyYzukTmDjRkcb8vV11XkQzXytmnLzApLYcd9pgUWChZnpff3S+yYpnIGyt7pK3jbDlgn1FZsFpkxciwHH/SMim03i4PPPS6hInI7lu9Ih35hdI9+SZZ0Sty+Q96xbV7xbJj+cpXBiVSPEYqUhkJTKDLglcUYPCYHLj3gDzwdEXgl3LCR3oNmyZtNANf/GS5VENf+it1USjrrC6Reb19Mn+dyFe/sloyXC3XfO9Fc0yP+cSp4jBHCu4aeeLVQO59uiF5fDlot1EJklSuv+lpKXurpMUS+c7FscxbIbL1dpq8s1z23q0ijy9ZKNjfkSKBfOrYWFb39spAr8jpp/5JsD4vt90zKC8t75WHn00ka/9JDjlwoVTSZfK9n66SouXLWV+IZOXAmKxe25Adtvm+5Dhf+teJJGldolTPm5dEZIFIMibSEEk2BNq85Qn/9pP/9h5QpcF/9WGAMolIEos047EGJWpUzR+RUQPw0cG00RAZa4zKSOVZqchjsna0IVtNeEBmdn1J/LFYlKm0bmRYKqnI966NRUVKucwjctW1z5vB1E8C2X37G2XT8TfIdlN+KrPGXyhnfPpC81mDUMQPFEA0+j8GabN9aTM+S2FSoqAbWSSxPCeJzJMgVGxNIqvXi2w9YbV0FqrSXQykyxXpzNak1RmU7tYlctNPRWpvxaz5m72dbKQwmecVshOJQo58X2TpkiHpKv5KxhHLpBZfPvvlflk4KLK4T2TVcE1GY5Etx1Vki86qGaj1JqePa38Uik1Nrr0tlU996TeCfaHc/fiQjElNllV88ewemb1NVRr68UTkioufEJtPyY+vfsMAdYJwgQw3npVI1smCVwIp8Xl51wFXy6DeYkMxgKwTjn9IbM6VB+8ZlbF0rWw+/Sey/ZZ3SV9UM9ui21+vBhKlg1INX5c4jeTE438lLaySNkdk0oSnZUF/IAuHRfoiketufFBcPi/nf2mljMSxVBOR0WBIpkx6SOziN2QkEHnlZR3wzpQjPnyjATgZeJc05MD3PSNwmTz0TJ/0JQ2ZX2nIrx6uSIczT7pLVbH5rXz1op9JTUQ+8qHXTFzao/P6ZG0US0MG5JlF6yWTu1s8FktXabWMy/ZK3hsW7CG5876qVGRUZk8Rc2yXjjSkGsQyKi/LdTcvFfWsfuObgyKyWirBaxKmPRKGy0RkUOa9IVJw7pbxlsjhH2xIZUPiXK3WL0nsSyINGfRflsFopWw5ribtiHR4Q3LYASKLhwfl9VWDMljVyYzIIfs/JlkelbueTKU3GZNRqcviSo8M6R06FbnqIi3YLZfLrx2UuvRIXYZkVTAq7aVFMmPiSnl+RU3gOjnwgDtlLFaEU4953Y+d9DvxvOfEtsZkcqdIDl/yTiD77m7GW7nhl0vEcu+QFteXbqcuv7lNZMGqSMZqIiOrdKDWP4HE8ZhIOiaiwXhRKLHeuAz0q3mOvf33/24P/J+XPv6iNG3m+c06bLPBruUQNVko8N6UQ7TviE2UDpGKQz5fxqaNVcNLOeydd+IH7Tz7xnmoh9crlWnLKts6oKUtS2D1EkcpnRO1yxwRBjH3PHMC9dow4wrtWNEomUwrQ31Q6hQ8VUH42kwMmxbyDW7C5mJEyyAbtjxpw3aqxHFEagemSx5FdVpbC7TnphBnVSzi46QVvGyIWK0mpcR0/P+FvatlvI0PxS1oXU+rMGognDa9nbufOJxPfihk3WAv11zSw7WX/ZGLvrcDJ31qB8KoTpSoDE/tQ0rU1OalQo/spgnIaZDzlAsyRsbrxWUmroKW0kmUc2qBV0uQ9gqylOxJFIo5arV+Mp5DS3YC6rosGFbJRArZlKImlUsLnuPTVWpBmt528tZEwmA1kR8g1QJOm4YUZ03dVTurqveVuI8f33gEQz0By5ZFLFg2zFZd32PbvXv4/Z2X0FKYpBQSWluK5J0ataEWiqV2ckXozExiZKiZ1p51M8bOrrCiNC0iniJs8+SzLTiu0Lsixx57fBGpz2bW5juwbm2OPONII2WHQN5Y7VNcKyTnRgz7/RRL3UTJKN2tmzCls0Yqo6TpePxchXJRdeqKG0goFT26WnNYYS8Fckwoq5tRm5vtjPlDZHJdRiFkeyljYyFt40Ki1KIqAU4+IbZ8gjBDxu40hp5UhHy2jYSA380VPvr+QYbWd3DPvcPcP+4Fjjmjl8u+9XFjQgkadQOqmtBVo6xOnqTO+KLgWf2gCp5Miaw7jo5sC7m4xnA9paOlTLFgEYSrGevPUvJGKXrjKFi6p9Vd6xP0RXjWABOnNNUvUwtZxB5lSnekAlSOP2IaUWUSV16YZXBdheOPe5ogXcC9j+/BfnvM0ooctq2GMy3XqREmQu2dtur9dDvffvxH9sBfDBP/kdf7By+iTTOVWWgiSdVkJGpOoqV0MH0oHQx11GnnPyKTV51pQt/KLTnpPZvRPzzEvfN2omt8hqzKsqwePLtBrQ5LlgwybtOFtI/PseCFSfi+Rybjk/Oga1yeXHEF2eIIVi5g3PiGMabU6kOkmtO4wXDS1DA31RemXqyuQPOPFImKuNHWSGVHbHHJeg1yuYU8tgLeWFlj7oplzFuzgDlLX+KVBStZunQyx3y4QoF5/2B/bPyWut7UlqYn/JuHQ58aHatQSVdy38I+brhzNXt/8E6mbtXKuae7vPA0tOcssq3rGZV7TVPPcYvUx0ZZsUBZH2ppD4njAR39KVjTsPTikdjortO0D1c0SCBH6lWopqPYVivFwgQ8mUxSn0hayxBm1xNa3dTWH86ypRZBZgWj1YQVi1Xu144fVIzkrzW/OXEYUWqBIO0Dbz6irki/A0et5rVJLF64iK9cs4hHlnq859gH+NDh2/D6k1fyleNzRtOMvS3zlitfO6I4bimvLhsi8ktIXCKrJkXpI4zr5It6R6vhOVnWDwwRWUtpBB7FgscRO6fYfV/j0OPHeGT+Nnz/7iXU+AWupYMojFVXkrWFqJJDzZHjvC1IhlVFEjNr5jD3PVPgxaXt/P4B+NEty9h2O5W+DWONacZjkTmvLMa2JiDJLN5YqE7A5QYUVsxtRqJ8mbhIFCjvu5Pn7rPp7NieiVs8w4rBmxgZ7cfxAgPP0vO8MuIgURdxOJ5163r5yQPCPct72ePDVzFt0zI/+36Va6980mBjMuXXTerRwhdK5FCmuM38x9p4Y94gZJ+hHr+MmoZWLhkwCevjWnIsWbgc37fJydaU9bhEQ3QUu3DsBtKYoW4ELH82adjg2xePsGBFwKKVDX7+mwpf+lYD4jyD/SFbbjfI86v7OPe7f2KXfZ9m/NRBDj3o5yxeobot1YI3wKmZpqWR8ijhz5i/VJfy9uM/sQfeHBn+E6/2Fq/RbNFpo0ydVq45OfQEadq+bYM5SK3EaKRVDJukUPGVndHKmjUJJ5+42mhvn3rsNLac0Gns0WmpwR/+4PLEk3kuuXY9377gGr54yiTOPf0dXHbRE3zj4lWMVMeZgVo10fUYYk9ZB1nTRFQDSbnk4GTyBqrUnEzrjUT11E15XXPgFkRttnY7sYLf1UXnrm8aTNKJWGovD4vYyVSCaBJBuBVB0kLoDJEYI4HyKP7ZQ6l8bx6GNFX/nm3oISt7V/OenQ/gyP0eprVjVy6+4pvsu/Me5HkdO1iCHefpW7WGkr8Ljz4ADz/tc/GVC7j04h+RJyBbzZPP5ynKePx+x7j8LM+lYCXYUY5ETQyq5E3HUyJPo7bW6LC1AegVY8iNIu0d7LhzmeeeW8mpx63i2UdncPLHq/zu/vVsNmsTJs10qYZQHRNacpsR+TGeHWJb4xG7APkBw0Bx2oSPffwKdt/mUv5w2wgXnncVhx2xr9Ek99dWMGVTh81nuNxyw4uc85X1/OnxmRy08zxWrq3QGCzgZus4XgaPfgaX7sDjD2d5+iE4/pgneORPY7gsxpIy/cNL6eqqcuIJp/Lg0z5P36Xcj26sgsakQaHoYKc1cnFK2XLJpgGFfA+zdxMef7mPI45Yyb0PFTn+jB/ygUOuYe3StaQykX0/PMZw+CLHHPInHn2hxrcun8v5591CLB24+ZqxuntpmdhvkPHy/ObOgKNPeYmpuQq3/nZfnnvyeY478kGWLmnelNXTWmytk/Eig5R9766/4IBtH2X1GyXOP+/rfPiY7bBpNTCyOArYc5fN0LXTCcc9wk33C9fdBXu++w6OOeIP1NiWTTafybRJPXz94nv45pV93PNwwH7v/RnVsZfY54A1SL0d1wkJ7Jo5t1RilzDMjru1k6WNT398BdfdkuVnd1fYfadP88PvPmjAYr/8w3L22/1qPnfcCt65x8Fc9b3PMaNlc9JqkZa2RFXohkdiKHlqoNmAY1X3o5Gu/rPT/+3v/2t74H9XOfn3ftuUfM2vaIcukiTVAlskUay0eRFtsjQagWkwznlOZO/Nlgg8L0ceNyLX3LpCLrzqUbn0h0/K5768VIp2j9isF4eGTG9/Qx58oCq/ur1fpnY+IA6Xy0MPKw6+Jo1gSBJZK5GsbTYLN9TUFO6exA1JRd9PAesKZtfa9AYw+4Y6tYHZp6lowIGGBCTSK6mskVTWS5IOSZoEEkd1icKahEHVfI3javN70vev76AN9WItWWtpT8vbq/r75MCjbpIcFXGM2Eqbb0/JljvdIs8u7zEV/aM/9ZpkicRlRGx6pKX1Mdl9jwHT/LvsByKfOPU+8bhCHr6nXyryuizo75ESFdlxWp9p1Op7ffPcOVLgUrn20tXmOIThmNSDJeLHi0XSERkZFnnnnhpiEIhNJDmWy7Zb3CxLVy0z29mItHF2mXRZ15kGlb6I7pNqZbnUZZ0My5C2jOWyH/eLwwKx6RfbWi0WenwXyGVXjJnXefjRXtlm5lzJMCZlRN63R790tTwsm3a+IGtGQ3m9d43stN+NAoOCrfsill22qMiMcc9Lhq/L03Nfk89+7iHx+K20MixqJelkSN65Q4/stfPj8vJrIh/92LXicqI89eRqU18dDJdJXf4ow6OrZe/tnxOLASl7IjM6XpU3XukztWQF8tdF5IRP3COtjEo7Vclzj7xv9xHJ2K/JBeetl0h6pRZWzbEbGgplfPanAo/JN74wR15fvUTeseeN0s6YfOnkyJznQTAsflKXfhmRfq1BH7VAHBJxLQUTJOZP95T58vs/LJcPE7eSAAAgAElEQVRU1orvD8j5n18qeQYMH6/AsJRYJNd+bU7zmMl6ufeRubLppGcFalLMGDW/fOzUq6UqIk+8slAyfF8+sPsrpjcUBhrwMCoNWSaXXPqclPVzk4rLs3LpNXdIYCr6FXnkmSHZZOIyDXoTPOUIxuIwIqccv0wG1uq1otdMvOE6qkkqNUlTDeXQIrVe528//hN74F+oov5rA/7f/6kNOuG/+gGVpBkBDzopddX6y1qsbJaclaVSTRkZW8l2ey1h90N2Y2i4lzlPLaZWH0GSAlm3ytlf2oNGXYglZu+9NmW//dVIXcb1duLxJxrM2EQnyCFBWMXLFIgCjUNSzrMK9HV6rJI8NQf8szqa6uY2Qm7U2K0ift1tyvNVZKayJbQ+t8HZpj+uVDJ9e8Pf+KsP/vf/o7tgo5qpuWvoaGvn+uuO59JyTCnvkCiIyHM59sSD2WpGhqpf4Uc/2pYprVrGGE/VH2KXfVuYvUOZ237Ywx67TGHixOl05Fy6powhcYFxrQU+9dkMM6a4pHFMI0rYeS+Xsz6/CbP3GCHVEoGylNUxFw3h2q1YuQFu+tUWXHHxWgqlPH495YRPfIwpUxsMjQZkMzHnfHlnHCfCzfRTr2npKUOh1EIS5ch4IQ0Z5tMnd5INOlm+bIBYqpSLLbR2RZx+ZhlJK7xr3wwXXt7B048FuEmZc77VwcF72axauAYvegebjBd+89sTuPh8H9vT/gScflqJV1/YjDnP78/4jg6+fem2dLQ/y+DaDPkWm912j9hyuyLfu6TB2FDEYR/emwmt05gyvUwU18klRePEc8r9/OLeXbn8ohHSZDUf//h2zJodkUSe0SwH/iDX/WRfxhd9hBrv2GsWe+7Tyne+GbDXPq2qPje8FNcNyOeznPn5fahZdb76jW0ILYdbfzWTn17Rz567q2xNjUQJqZ+nWMgyMLqKm2/dkgsnDVGtxpRK6swssfcBM9jvfSFD1dWU8u1ccNmm2OW19PdC1m5l5syUUz+7OcOBomtb2O9dDt+/ppPHHoOhao3J7UXOvvR0E3g8dXInp5+xDZvPdLDd2HC1A4VL5XKc88UdyVgWi5eOMKl7Nz5z2lYkUZ0k6WDP3S2uv7mdu347RratjzSyyViDXPDNzclrPVpNXaaopGuzjdd1Uz+tp/Tbj//MHvgvGV50Y99c3jcH6SaMyE5ck34dJIsM3UzRmX59Am5+GHHn4Rnn1ZZEiY/jeKZe6WgOHctASgRWFhFN/2hQzE4klSaUyGEIkiGitAPXUZC+ZRxYZoBWKLqt9uxKE9KuJ9pG/q856XR7m81EZXEYY46BIeiCTm2EzQACFfkbyLs+p25LA4TfeGCUeqbcX4Os2/jk3/2q+8Qgc7TBugFG1KTEjWBJm1rBwKtgrGxSQivNWiZRipmTZozhUlNZLFdt4jZEm5r38pWHLHX8eg+5zAwsT6iHWUp6X0lignCEfN4nTBXqU8GKJxiQkxLsYhkidApUxtbT3aop69ryXYXD1ow2sk3bdqg9ox4KTjuSVLCdMcN3tmxljHt4Wmy3syR+FadUMFZ5bZhG1lyIWki8TchQpVHrwdESgj2IjxpvtjG30J23up+Vi5eycugMrPwcoxcveLOasP3UoUKVvNVmGDGOO2JYLHpcEsYRppCxFpOx1Lk4bQNPQxN4lHi3waVfE6L2NRr5a2rzZWsQkblgdWGzHUkgRGGBXN6mYQ2QNbmSavdXR6mew3rDaJjt6huoUSxmyXkKuoqJCQgag2BPxckqH7qE5tzY1IiDFnOcM9SpJyvJZKc2mdRK5aNEELSTycbmk9hhJ0HVhVyNTCGPxvFqi1QJqZoJXEn6KDjjyaWJYWTHqSYnVbGSMjXHJwxj8l6LaVJr6ahSq5FhmiGBGIu6NjpR7KtOqpRp3kNWm8Z6XuX0/K1hWf2kwXLsbI+hUdr2zoS+NjGnbxintSmtTmO9bvRa17O5if39uyf929/4l/fAf2FG/dfb8udBWk3RestV96nkyKr9Ww3SoY2n8KC0nerwLOyCENhrcdxuKtWIllKDSt3HSRTfGOIWlaehKt8u0kaJWFnGmj7ilEnjiFyxhF9xNyA+9VxUFUJzm3QO0ByO/3ob/+p/JhDAMien8hf0d20dlNWBpbMJR2vf2iDVP4qi1JQS/VB6Aaup5x8/mjieDRu0YZWhqg9lf+i+CtNOg52MahZOVrC8PkNG8yMb280jfo5s3idoWGRdTSAZj5sJSGXYJM8o+Mi1MziFAsIQfpRSznSZRqNiM9X1HscJeXcKiehnKJhjEkUKNuogLxWKrTOo+w1qvlBqm4ZfV5RxndZsgTQHjbhmmMaNhoMkEygWWoiMpV/327AZESWfo+L75PMFkw7jR13kvU7sqEaalMhYE6kNNSh3zCSpt2C5YyZKra/yNGluAD9JKbozDfe7ZvnYKrcJLPK5NkZHh2hv90wyjqMDkwZCxOPIZwZNrEutXsD1Rsl67VQqtokCy3qaGpNCi03se7TYeWN6qsc5yrmdqNcq2MEY+baJChRsYqLDMnFhKQ7jzMBcqwgt5YCqNkHI014uNlEpTo2RykLacptge93UGylkAxrxq2TcLajXXPLKYIkCJK919y7SqMRodYBMOSHjWOSyMbF4Jl3dYRQ320Y2p6nrkFWwlR/geRZRLaW7mCGIVpN6FkHaSVavhySHijRFfEoZy6TbuK4OpTVairqKbBDXslhJhrge4xaUO5IhTEcgzJAruaS+RVBRDHZKLtdFw1HIWCsZW1VUbWSkZOrrOplp0vaaM2lzGotNaplpzT++AN7+7r+0B/5LA/Wbs2m9azfvvJpCoQOl3nWzhpWhA5yiLLUkEYZZCt5kwwAW6Sf0HXKZIn44QKnQqSHPRJGPmwhxYFGrxGSzQs7kFApOqkvW8SRxnVo9pNUpKgbDyKwcTxURqrJQeLsOklqv2PD1z7tt4+CpT2jkkWk1Yimb2DThdDTV6W8TTq9SJx2cm2kb5u5jZu1/frm/8w9VfBgWtr5/803MTyrGOud51Bq6zQq3By+TRRyfgGV4znZE6Ri5nHK9FRZVIggTcvkSge9SyOttyCFthDSiDIWWSaQsJe8UifwaoW/R0qaHP08cWkjYimO3Nm+cFkRBgyQsk8+10BgV6hLS2tZOLcjij1mM78hiRWlTVdCaJ5SI1OogrwxmRU2nMXasrJJCc/faLl6+j6rfMM870tFsKMeemenhtdDWkSOqZ4jGoNjZYm5WV/zovRTdFopl2/A+6pqRkA5TyuSMJNEKa3S0txArOdGpMTC8lgntW5iIsEjFCF5CNjeKUgB9XxkjIVlNpVE0tIZVZHUPdJsFVBArIGw89TAho6RGCoSNVWTc8YYHXiyWqFvLSKRCGneQKxbwq+qmHU8YC5msUK/VsPPD5IoucaWM5FyySsIVIXJfJcKhUNyWIFA4WRZfxgzPOggatLRmEVpMrmVNYoo5DztNyeS1OesQhDF2qilGKZE7SsQopUILaTWHp6ssZTEo4VEVp1YWVWtSH4dbbCKSatURNC8hSQMcqeFmXZJa1sgdo1BZ2+qstU2gRqxhCE7GyCN9X8tDQ4gzhTBQJVGA12gnXwqagQXmUtBJzMZVqHpsVTzwtoX871z2//bT/4XSx19uU3OQTg2NTDMTle/QZlgBTU5AilLOHC8liBr4YYOysykNPyKX15JDhONFRhtdyLSDW8QfUlyp3vVHyeRbjQJQp8mhMobK681A6tdcstl2Mwlu4h114FX+r55OutzTQVlvJn95Q9m43YZcZMZQpGYgSSbjzihodWaiUsHmYKlxXM1atXIO9EbUlBxufKW3+qp7RB9aVPmr6f2G+0Ss2mR7AMcqQ6oIyxSxB7EThRnpjOxFM/OO/PFk81MgVd1vhOXWDR/CTXNNvbFW1dP1ZDOtpFYPtt1K1CiSxC65Ys1sdxJjyimep2WWKqQa1lAhCHOGdRw0HIqFMkGQkCuMESYJuXgiZIcYagwZu3wu24ItATlFbibDoN9XuVairYHQfDW6+TBPGoxgl2ziVGmHk6lXK6a+a3l1otA3n1HyJjsHDYEMFJNqa+hDH3GlFTdxSbIZYh2YvAbZQkQSp+gAnXX0vNL3HDN0OpcJJvsyX0iQUNGgeoOsE1ZboLQMN9NlZpQ2KjfU42pTrQXkIiXOZfDKGWKljhbn4blTCONWEncdbqzBDEpMHI8TjuJaOappTCbbIFOfzmjSoNwywvBwhY72DoLEIonbjNLHczVk2TclvLHhHjKuskss3FwHBJohOoZbyJCauLCs4UTroUnSnmYmp50jkYio4ZPLjSfwBcdTTb+W9rIkaR3LLRAlNRwp4mZj0mQY29LVZ4idi0jsHiTqwE61ZOjjZPQcbhAnWVxX48USvGKleS4EOkNvYKk1R6t/ynM32AUxJElzPf/5NHYNjPW/NBN8q0vr/1fPvdXI9H/4AXVQ3PDH6KZ1gNNx0iEM9WL2cBQhZw5xgdbCFiRqUHDzRu6WKHPaisgqIMfWcNEGuULV6OrdnM6WRsDVga1KJt/M9tNsvZw2gERPNi1F6NC4IVjXlCj+2cfVYqZuYorlKoNkCOxesPuw7BGjHdXAUKRsyh5GpqTvoRmLih39Jw+d0WuJo1kSevOHdWBTIqA4dezMEDEufqwskOZKoXnn0H1XJVVgk0ZkRxqv5Jh9UA8apFEHcb3YzDzQFYSMI63nsQ3RUN+ggK14z6RE7OuFWTRpK00OizYWB6nWHLIZmyAZoFgsGgZxrqCN24qJPEsDHd2LZL2JpqGl9zodPCStgqNyv6h5URdGzE3OLVSQwCZR0F+sLJQQS3GcjdgYnExYqozgZUZJXS0/2MQ6jU5ypoGmM8s6w1AISPMRTibFzVfQbVJ2tGuXjeFHzwGDW3ZLZJ1uvKxlch6TROuoFtobiWotZAoJniHW6U1Kg4XbqNe1vp4jlxtnGN6e5jdWYhO35comSNKKa0PdD8i4E0hE97EGDpQMqc/zXJK4aLTFhaKWI7Q040PagRWPw3NtQ4nUkddXImAE+YJNrljH9TzCseZlqU3dJLJJlSneXKQZI5eXafZNQj+DkwZkCy2G7pe1LdJGoJ1GImWOJTliQ+TbwFSPNf2ns2n0IkMaVUyt383oOdgwK9OomoGwFc9p/q5X0FBdvflZxBpIoHxqpRu6uiRplgL1Im6uTDeevxu2f+N/3/76v94D/4UZtc4q9cA1D97GmnAT5KnFgipJVMBxla+81pRDFOwSBLoES5omDaXi6IlqCVo7db0slWFoaY9AXia2FKRTIqMpJKPg5dfqlIqedQU++J4H8cdybD6zyK8f3IeC1vEaIdmczprUaacgd8U/NuvKGwfMJkO7Od9V44ijnGDLJQkTnGyRJFIRVUikiR4FBRGvNwnUkc7AghmkmQHsTB95mWUOUvP13uJ4aS3aUiaw3rSa22BWkCZnT+FELoHVh0MeNxwP0Qh2YZjE7yay8uQ8xW9WkVhrrAGu3Yukg4zZBVpr7cT2pKaDULfHJOgMYKcWjjWdqJExkWSpLMa2O8ErmJWMk7aZgUinv32hQ5fiPGshmXyG1O2lkcQUmExQi8k4CtKvmBn44FiLtgWJo3YK4jCxM6CRVoijdZSzWxFFVRJ32AQbZJNpBhtbz9Wwqs1AXCeTkI51U0vGKLV7pEE/ltVi4seSeELzhinN5mmss1cloDKIH443zTu70mZC3/0kJFd+HsJxkKmbxmIgGjgxBc+KaFQqFMqrsYNZSHaQ6kAX5Q4bPxolSm1KOfXTaGBClqhlObVgDTlrMrnMpmb2rjfBUmvIWC2hsX4c5XI/pc5WqlGkUbtkMsuQNEclGkdRJx05nyjMY1U9rEKNNGcRSEIxzRCkJWyxcFWUpBeF3uQSpxnhpn08R0WRMZ6SHqPFuJ5j9OA2U3ECvdEF2FaBIIFM3scPR8lnRojTKm7STegq07vNdGM0+SVM15D3ND8zxRENRYAxb4VJ9CnQA/VWyHaZ6zUKEuxsM2g61DKSGxp8qWWCkz1SLcUo9lVPW11BGYW2Ye6aRrKmHxnW2Vuc9m8/9e/tgebo+e/9zr/5028O0ht/sdnC07MyIQ6LptShzThLC4ZJK0mUIQldeldnWb3WYsmKiBUrhflLFjF/4RirB9YwElRZ0VsjTKo0QrWP56hVIN8WETs1GizmY4c+hERT+PEvD+Thx+fxsSPPNY2/TNY26jqdBW8cmDdu299+NYkXShbW5JmqGnXaqNfqxiizvidnasc689W6sCZrZNy8aTxJkBBW/3kzUd9PB3H9k5pspI1boEUZi9hSF9o43NAjrEGS0XpwBsfNkTP3QFU66IpBO/0Z8EtgdRg7trr31Grtqiwx7TRNQ1dmmBVMFPYTRw3CaBQ7O06J90ShNnFbmqscPWyeQ0epRrU6SCabMVGLSVrCUQNNkJJr8Y26O8VhXU8nu+z4dWa238hWEy7ignPuI5WqYSVrc1clfa7Xbmaf2pJSAHIgVQq0kCmVcTIxo2vqVK1hyq1toExPR29Ga00DMUGjpfpxnCZZ0Mk3jKQPawxROL7TglMKqNdisoUMftptAnrjWIH/2rCzyVoxod+gVLIhnEHq9mDRINfiUIsj7IxHxksZGRwxjdWIIaPQKGaKpheQpH3mGBWKHkFc5cG7fDbb7Pfc9EMtm4iZVYtTIfJVjSMUs6lJ2gmiFC+TM9x1x2qjEVcJIx0Ay3iNKtlAcyIr4A6j4TKRY+u0gNCuU02qyv/HCoTAUhFgTFaVTaMWjUhzJVVR45OxhrAS5WXnGfMTUruNujtMxiqSsI56TTGk4BrCnZIRtZQGjWpKi6fbnzBSaSEtrCRJm5MO3RhHSzxxnUy2hdR2sTyfhq+Nk6ZEUhxdeY2ZlZG5k4pOgLTvpOe0rp7ffvwn9sB/oYT0t/cCPXgqUWou3xzt2ohLXG/BzWoidT+WM8w9v6px5gkPMZC2suOOEFQD4qTKuAkRK9euYmTlt9h2dp1HX8mSS7ZGrBKB8pOZT9bdiVim8afns/jUqA7qxOFFklhtxCpWdok0EMDkHTbVFWp5fctChSo3Ik1xKZmar84eCtkyY+Eg3Zt0mEEy1cvHJJH4eFZBKzmU87okfjOh+e8drEQHWB2stUq9QUetzTh9pKlLJAGeXdalB9l23XNZ0mQCqTWEnV8DMpvIB10gROrArKbk3MnNpWlemR8aYGpRr8YUil2gdt9kU8J4hGJZS0q6aiiD4xNHPmms6SUZTd9CbfatxfUG5h9JDaelk1jr4/Y01NnWu34FrZ0OfjKL9+z6IJXaocx6R0gcrqRzooctHaZu2l6ejH5OnfXV/fEUixOardf8iLnAFWBv1y1auzfBd0Y0DQsn7xGLys02IY6LprYdanKUl2IUnRq6m47hMAnb1WAHG4ldCh0OvZUqba2bGaWD4+oM1cNP5mFn1pPPTSBN27EcbXJqnqHWcqHhO8QtVXJOC6WONpK4h4yTQLStRg9CZogoHiKXX8QIA2TZl1q1QJ0PaDnZqCXyuXH4SUw2t6mZCCSswZdess5MGkFModzka5Rs1eIXCOJ1ZFuKJEnVTFA0CSyKBvBKK8iotE/PCqdM0JiBlZbJZzdDbx6qz8+0Oobp7utgnVPMaQ7bWUHS2JSWvM6Ii2aFmkQlxB2mVNRJQw2klcDXBqs2wXvI57LEQac5v9xyljoj5Lx1hOEKknhP0vIQHlVGa+toyU80/oF8wSUM1IHZIDWI0yyplExD2wgDzPm8scz5t9f/37sS3n7+H+2B/8JA/ddv34zr0aW+RvfoFWCKmjgaM2FpE1BTLuoc9JEpBOH7Ofuclzjk6Cmc+YXZZqanJYjR0Obg2TGN2GfxwjamT9fMPYdC1qOeTuKlV0cJog4mbprQ2VHkwbtGcZnOZjM7SKMiCrjJZAqkjmqP3twFbz271tq3zhIg29oMGlBhvx+5zHmpQdostpo+YEYBQYlNNBKxxTbQPfnN1/7rvfDm/5ru8ebJvNFJbpQgrm0693rBPvEkxBmdCCckYxa77eKSbWkw5nssesE33OsddhPiRHW2Met64fW1WbaeXKPSSOldW2GPfSby8hyf0bE8xZZ+3rFjC0PD8Py8BgWnyKabF5jYmTEqAy0rBLFDoZRD/PFkciP0jvq88cIAqScEow223zHLxO4WcwRffL7OSL0NyVe4/KZ3kmGEXbbRgaKBW/CpBR6emzGqxXXrLVasHiTntKNZtqXCADttp8vtbmqBzcj6DhYurZI4Rd69d445c1MqoyvZ+53TGR4WXpm/ipaWFrbcSs00AVF2DUuWdjHUH7PD7A7E7mfJ0haqNZ8WL0ctSpk+CTafOQ4/qrF0eY3BwRzb7Gyz6FWLhhWz8+yEzpzNSLWDB+eMkFBkp21amdw+bHoNluMYP6aXG8+jT3qEyQQmT3fommFBIWIsXUvGaaPRUMRAmZee1/5ClXfsXSSr6gtts2RTevrWsmZRwviJbXRPaRDHWVJTr7bMjfapJxOK5SLDw2Vy7hS6p40wfTLYLWUqjYQyNeY+PQ5fPKq1kKlTMmy5tWeAVa3tOgfP09eXo2e1fu5R8qXN2HkXHaAzrBnqY/4rOTabOZNCKWDJMw7Tty4yeWLWlLleeXWY4agdx8ux+7YOhYxvlFF6s1jRm2fFsuZERhKXrJ2y5555onAttpp3zFRDz1e9hDdG7709QL95lf8H/vWfsDf+O69h7NqGDDwmiZqgA/VLh5JEajKuSJpWxY/GJEgbEkgg1/2gyfX98jlzJZABGY19GZUxmbOsJjZ/kPft83sJJDG843qQSjXx5dB9RIpUZMmQyE1/fEhKPCXf/LzSIIekOhRK3IiM9VsZwlFc/wvUaRNv+ubnSSQN1AZblySsSeCvllDUNj0gQ2Mi49wHBXutYDd03Su2arAIpK04JN/50YBu7QaL7QZ/+Jsv/Od/6XcMTtX8SLLBjtu0kK8fbsg3L1wjZZaLzVpxWCd51spZp70ua4Z6zK6bar0qE5gjI4MbqZL98uMfDIhLTX54hchJZ18veW6QC88V6bYUY7lOyuXfyB33LZHTT+431vMMw3L4RxbL0tX6WQck9VeYDRjujyWoJ+InDfnyeU+Jyy2GJ53hD/LRo1bI6jE9RiKbtK6Ugulu6hAyKtNn3CgNZVBHIvVIpOYrMnNMHnt6RKZ0PSRF92Up0yc5eg1L+/nn1ktN5sni/lAOfvdyyfOq5N0n5MJzR2R87iVpy1wma9eKPP7YSsnxZfnQgbdLQ984VSysIkAVc3qevPTSqNz+izHJoJbypdLOSoO/3WtrkddeV5t6IAe+627J8Xu58jKRTkZkHOtlLFBK+rB89tPzpc1bJw6+HPK+dTI4NmBwqnVFtkosP799hZSJpI0BmdR9u3zjm+pS6pVvXTxmdr6ie9Vq3o0vE2gY67YiCarhgEG/Xn31HMnxBfnW5+aISFWqvjK1ByWUIfnBj16RAgskx4gUGBBd07z3PcOydtGQ+LHCEEQeeWq5jLPfkBY3kILlS5u7Sp55oFdiWSp+ukaWrBqSfbYf1BakRh4bu/cNP1soo4nIc/PXSMH5nuy6xXr51LG+tPOKfOtrPVKTijz/wlIZ5/5OHHpVuS333qfIUsUQN6Rn/bB8cP8FkrcGxHV9gdWS5yX57qXzJR5Rk7hex/qnKqk0kQzGOq4H5m0H+Z+v8//tP/75lO8/cDP4y5doNsq0aaIaS51R60Nz/nRJr4nOBTynhWpd1QNw4mdSPPsdnHXaD0jjD/ON73bgJy5FRzjyw5OZP3+YF5+y2XH3Cpo6/fBDA6wdWsshH5nOPbeWOOOMl7j8ijP49JmaVN1OMafZdKp9TYwZwDaykw2bsaFe/Ob/NPTZxkrsZlc9r+DHVkaDiHwZPnnGXqb2aFqBqaZ8Z3GSLHE1w7u2Cykz0JTv/eUL/o9/q0KkWadWapKG1Rp5NrBy1TrO/9od7DjrLA453KOWDPLrm3u46ro/8t4jjmL/d0K+s4wXehTGJVT8Edpymp2YJ7UKlCaBM5CjQZbvXvwq7/7gRMoTx/GzGyZzzAeepGAHnHLmUcx5MeTXv/kj73rv+/jMp7cijVRP3EdbZ8BodRJXnr+GH1xUYnzHDnzwKI8XHizxu9uLxMUnueLynTnylAzXX7MazxrliMNnsvnWs7ATjzDVRleWRh2KbpmLvn4NPf1tnHzabAq5Ir2rxrjr16v57DmvcdOtR/Dl0+7hnke62HnXyey+00Quu7iH1LHxcgGdExNWrlQ9sMu48e24GVBYkeVlyRRTMlaOMCxx2nE/RzMiP/vFrSlKkZeWVrnvtz/nhhvGcelVR1KYOEZCG+d/YT5HHTuL1kKDJJNw9hcW8avbXE49eyqlgsXPfrqMMz47wVDlOrsb3POrbTnuqN/isCmfPOWDLFvTztfPvxo4HidxiO0hwkTNPxFnfX0CV140lxtvnMapJ7TieA5r1sKzT0xgl60PZf8DxqvLiFADaAvDhDKJs05dyNTJR3LoSaBK1Ef/1MsDD32XH940lW986zQe+CMccfRzVNL9OPyIPgotcNNPnua0Ty7jheVf4I3XhLM/PZcnXr2b/Q48mC223Jobr3uZT534CIl1KlvvWkNcjwWLuljf/xrHnDyJd37A49GHfU479kkOOGp7Jm4y3ljYjz74BW6+9Z0cfHjMz3/rcN+Dk3nnu7PssIdtHJU3/vBBzvnKY3zo5KuZmqg8VbEJirwKm65YLd3pKnSjcuB/nPNvP/Hv7oH/84Faj9Vf135Vp6nLKBW5CngmflvdJ9hqMIhjUqtBXuOX8FEJ0nGnlih3ncynj36J1dVHuOqajzFlWpavXrQde24VcssPYZc9y7hWjVuvn8ML867mF4/dxl6dPiXOpm/kOc793EJmbdrOaWd8iKyrNZDyks0AACAASURBVDbXGGC0a76xmfdWJ5atJXQN9nQ1KTxvLMPZTEpDethkdj9pLWc647Gz3rjSnKSd2rpu2tu6cdU9pwXof/BITfTWBtSrFoJMn1MMx1hrzjgz8YoL6dysn49/uJVDj57CnXfMYtrk8c2flTYidYERkmrQqPblXXVqqlW8Thw4Gm3L0ScmfOv6Lnynzh037EB7W4kf/GI1B+1f5sc/rPDMM7Vm0LDWei1NCw9IGWXtmg6uvGgJEzv35YIfuHz4KI+XnoGzT6hz+02PcexRHXzlkh258dbLKJfXcfVPL8WW2Si+xbK1ztyg2OLh2K6Rinn2CN3Te5m5eczhh81gu0s6mLTpjjz78DD33t3DbnvO5Me/msamkweYveN0PnP2S5RatMzhkMQlbXkROmuMPdvzlLuhlupFBnWaKa3lixdtRWHSKB89zuWOm+9mTTiFhDcot+5o6quF/ESjEDrquC34/i0Kee3m2YWLuPZ7zzNr+qlsvUMVrLlsudW23HZTjiM//Ap7HtzDlz62LV3WKZx29S2c9RmHvqHZnHVcyMP3lEnspYTa6HOVJtfCWRfAdy56nHNPmcanjzvUuElfeTni3t+4nHveXuz23gVoEzRTKtGgmzDMI+nuVOsVJs1cxo6zs5x4ahu33Zjh3e+ZbWr2l130JGFtd048qcz3b2ijHvXTNiFmq032oYHLg/fAi89uzvs/1MHVN/cztZBjq47xfO3ivTjr5Ke5f+7+RHGDTScFXPGz2ey7v54pER/44K9Z3bs5U6dvz4QtH8EP2qiGe3Pp14Z5z+H3MJy8m4wzjkkTB9lk2ko++elt2e+gSTz57DtwtWdiDMZmQWlKkypLNVe84hM2KL3+wen/9rf+xT3wfz5Q63b81WBtmBrmlmu+I0kNy82Z6bPOXhX5qUaUFI8o8si6WmNTclOOOuspd5ZJUWhQTEvZ5fBTGjzz2G95/KHt8JJNWDF3NqedcjGu7XH5jTn6GlCrFEz3esIEbaYNgNMJmhJuW1g6fd3w0AH7Lx9as44stairhlSByC7KylfgfhB1csoJP8RiGNHuoa4OdJRN9GJtIzPufZw0azs0Nu4fPcyM/m9+RmvUru3QPWkqp56xmh9+9y5ePmE8v7h8dw75QJHPn3YYXVMD6mlIYrcQpmP41Mnlxxmmiao3NDHYcrJktcnEMGd8fhaeM0wtbDfKhU0mb8ZB+29BRuo0BlcbzXe+oHzqOm5Bf0ZNxnkyuQTP6aNr5p84/KhDCRoN9tgjz+7vHuHFRW04VrNOnS30k6pVOtaKaM6ocRI3MXX1ppA6y2fPOow5r/yMb33pbjy2Ys4p3czeeR+OO9Lm5utXmb7FwQfm2WRySJAWOeGTNudeOoIdFKhU1Pmnat1x1Gp6RDDqIFUh2DKVPLsRBRk+98VdueryQT57TC93/PIBAralyM5Gt52kLsMj6w3L+vTzZ+EwRk2b2mE3jhezaMnLnPTx+wmj35HJnwQcjJe20cYMOif4hD0e53zmU9QaIVPGjee0Uz/AA/eMkYTa5NOZej9ZbeIFdS698mAu/GI7V3414fAzA26+6QGmbbMDexys45ggfhduvoN2qozRxzcunsJ55/6JL5z4CuPHb8XJHzucwz76VXac7TCmZqHioOGWnHfBcURSwY1KXPXNg42TUXXlXmEUj+kcd8TpTCs3iOujnHneNC67LEOxLU9UT1B53k571njX/jFp7OA5BbK5VoSQyy96HfHuJ43GYefWs24sQ4HDOOKAKi+963Vuua2H237pc+89MbtsvwsXfeN9xL6PqJ56g7rDVLHNuaxdHHXEbAQ2/aMr4O3v/St74P98oG42D3VT1NWk91q1LrkmwUX1QbarDb1hbfObRONU79DaZreaGt3E9rjlJw9z9snzOfcL7+arl0wz1uIocpk4KeWwQ4b56XU/Ys7iI/HHNuepRZP49s1TmV6GaSeodCLEsrYBa/vm/lD3oKo8VeRvbOR/H5qkumY1HegmJ7ZS4hwaQYCrLi8ffnrDAah0K01s4sgi4xVIxSeIhnnHLlti+x1g0mv0rf/e1FqtxOptVz21Dj/qKouMKmZKt8ulV+3G+w+ucee1u/Dr37Tz7OvL+fl9PjfdOo2dNm8hJz5+xsVXvnFcwXItGFc2107quiQ5xTcFJrF9qhpo4j7yGsiqZRpWIFYbXikho2kflQYOHrX6MF4mQ9521FeCnzzEtK12MzI5SyKj983mlpOhGzcsUiSg3amj+TdR4GFnA1K7Ts5xCdSEFHeD0+ADH5zC3Q8dzWuvljnjeI/vX6fCvgUM+QW628fwWEsx18BTK36kMheb3ORHGH35XUhiNV13lr5HRxPSny4gz2zSrENdVztOma+es4jLrvwtLjtxyx3n8uRTa7n+u3NJnL1IbSjlfVx5Hb/SINAJQBzQIRMp1Cey8+6r+cLlBzKwbn+ycRstWZ8tt9+COik1KzE8LG0rW9mAKhUG854ZKL1slda4pamocTyG3ZjDPzODs08X7rjHYq+TOvn9bxbw+TO3YOddXOrSS87pJqMmkoxHPjPK577cYPtdZ/HIQ11cfXGDyy8L+cltL/Djazp576Fbks8kNGQQp6CW+wQnr2solbTOpyhlio3NaDjriQoTcdHS3jp8u0Agk2hNXFqLVSxGGRp7iiwHG2BYbI3R0p7T+AC++/1ZtLQegx13km9LKXYJmokze7NObvxjwmMPdnPVl9fy0F0NHrxrCS8vf4Sbf3IOGbuBa+t26P2nTfWjzetMn9BB++2eYnN//C///q/uxr+csG7kAqg7TrXTRlttr8b11hIlI9SrDaP7/PUt8zjz5Ls472u78NVLtjYzl9RebFgSehYk9kJga2793kHcdr3OrpbRu3YpiauUrzpBoNyQkDhSt1xT19ksdTQnwP9s/9lWg1hGcTI1YgaxM4PYXj+FcsCxJ+zJ4cfuyNHHz+ajn9ieIz++GUefsC3Hnbwr2+2YwZcl/+zlTSlFZ9B6QlsGjaC2diUCuixdMsAh+8xn7asHcvWvOnhonnD0x/+/9q48ypKqvP9uLa/qrb1Md08zDDAOwrCJwgg4UYInBmKOimBEk6gIkWhyTjRBkxDjgnLcIYQommCCoOKOGhZDWAUEZoCBYd+cYXamZ+t+r/sttd7K+X23qqd7BpkZOJB/5p3Tr/q9d6vq3lvf/b7vfsvvW4CnVkxh61gPqbMKjnZgJTU46JNY4oeX1vHVz94NBWYAMn+CJiQNz2LZqAacrB+sme0IHkc/tO5HWdKnGbFSR8qKO9qDo+qIu2VsnGJweh23XfMOXPhlYjaFuPK7Ga776ZGInPsllIzx71FrARAfIJEc3Q4TlfqgUxuhGkXmknE7+PzHn8VFn3wE73nvCH794AB+cDVNGfPxP9dUUV0whi7auPKy/XHzry2knsbffHgbxla8E/W+CF6th/ZEH3R2IGrePMH56PcOxn/921o8dT9NPh1JRvrJNy0cMLoYdz1+Mk5791y89e1MOGK26irhGXFnLhPswfh4Hweg7uyHKbUSsbcJzz5xCp575FiccfoJKFmLcMm/AM+tXwsXKWLnVrSS+/ChP7kPlqpi9ZNzcMFfJYgxhVinEjwUt/sxsbWFqtNEEGxEB9/GmrVj+PRZDJebiyl9k2RzlqI3A3ZTwLVsewqdzfPx4VNC3PqDufjCF0/AjcuOxic+UcLExg14aNl6uBS07WugsAjnnD6GFMN4bksdZ7y5D1/8+LFA9ipEqoU4nYML/3kDHriPNrPDcO7ZDLnchnZnNZIeEf1aaDToZ4kQ26uh0MDmNVsZwIlfXLUdp595NP74ffPw48vHccMPevDRxjU3dnHmOyax/8hB+NkNS3D9zSfiyEUH4fqrDsXGcWrOhDXg/JNmc3Yiu8sQsAof1G6Xwb4Gu5mBl12jNvefLQ8Mw6YRU4sN1fG5aWKq6hRavR76ykdB2x6+c9lt+NTf34TPX3AOPvGZ1yBLmae3Dd3eAN62+Ba85vgyvnTZifjUp4/Hl7/AUKjt+OQ/Hog3n9zDRHccQ25FstrYB2HONE3sZGbYzfzAYkZbCokbFqwJlSLRTbR7TQw29kOrqSTt2lJmx0CsX5Ym0jSZuFwwL/yyFOPuijYJ4iSGIzZ0oDepseyuMTx990rc+MsMkXcvnnn4TWhgCZTagjQ7EFHLgVYJ3nbyP6C7xobb/jC2jL0RhCxyswAlWTxtpGkKz7bhJCXRsNPYQ8liSWCg12E8cgclP5NU5WrZk/AvZm8uet08XHr55/B37/dw4fnP4Lprb8X40+/E+MQ8fObi87B4sSWJMElADJGmIBdaKAvmhudZKGcdYdoM4lp+z2O44941OPWkH8OpVtHtMQ3lFDi6gRMWn4KPfez1uPzrZfz1+57AoUf2cO8tDbjuIujqOsEL0UnESox47O45OO24O5guiSeenMCWaAgNa0TAj1w7QmvzATjvnNsxtTlC0DkcLoagQ4P90ahU0cAhkj7P+GmaWA5cOIiLLjobn/qowlfO34rv/eh+PPPAFN516mk49jiNicnVuOq643HqMb/Fbb9o4ORFG7G5PY4NmxYKcqNLDBM6jm0L/bURBMlK1PwEN9z0brz9rZtxz1Ifp5z45/jclyeREB6VOaJWFdABemgIXMwvbn4CHvbDTQ9dgvnzjsGqFccCWICa1yeJYJd/9wKccdIYHrq7jCWH/xy1yiiefPAEbNvSxPkXD+H0D/hYuiLBT3+k8NEzQlj2Mjy1xpOsxu/dvgjVyhh00o8oNGpukgWI9XZc9p0/xBkn/hbL72vjpNfdgLAbYeuaQdx8/9Fws3XYtj7F7bdswaPLezjmqINQcn1s3TAiOQhMmxHzIZghyS0h2QlrygWA4NxwF0n41H2vlzoDsznoS73anpwveyRyTi2gTE6JDzNBnKaCJd1fPkziS39y1W9w3rlfwcCAwlXfvRZHLjwTf/R7F+Eti2/AksMuweNPPoSHlq9ExXOw8NA+QRDpqevxmsU3YKBKKMiFyLKu2F5pTuGf+DlyXA1qsc8fNz17EIwRdW0yHhs2qijZNcH2HWjMEa2+Vi/BcVMw8cyvAKVSCssJcqCh3WsUVKbJLEWrhiVOS/aA1zvs8BH88JaD0LZ/ihvv/E88fN8yrJ14Pz7zrTtwwonMQGziV49pxOULpYTT6mcncehJ/4sL/mMDevZ5GKGzJ94A316JcoVeeYUkHoPlXI3IulEwtJVKkCjuQJajFz0JRXOAHQj6nmUHYBLfmX82hCv+exJB/E2se8DFuolv4/yLV+Dccxvoq3mIIqDctwqVOSvRmgxh2xRetuBllFQZFjEpLBdX/OxU/MHJDpbfuxJ33rYdTz++Da8+4iJc8RNg2BvH+V8YxIf+9lZs3PQ1/OaWa/HD6+Zg9MDlCLqbJWvyDW+xceXVx+OJDf+KFSuWYtnDv8bXvr4YHzyriUjdjr7+9bjmrkUYPvh6PLj8VoxteAiLT74F3/r+Ybjisqtw981T6B9dgw6uE9xu16I5I8ZAVeGsv+zDv/9wEiu3XoD7lj6FD37kSFxypSfgSsO1Q7HoiKdx67MT6OBuPLPhChz1phW49GcdpOqfkKnbiIYh0KfcxVhZGT4CHLW4isS+F456GsPzn0Ot6kGpFjLvt0jDCqAHxDYQOc/h9idPwKR7MVY9swH33HkPVm28En/xsUl85OMHS+LOIQvm4eYHToA7+nlsXbcdTz30KI56/ZW4etmQ2PaHhxQuvcLFB85+AI+vuxBrNtyPwLsaS59ahCUn2ogm5iHDw6iPPCpOPs86CK7Vj/3m9uPWx96AoQVX4+FHfoUIS3Hn02/E/gsJQAa850/n4auXNrB16ibccddS3HHnz7Gh81ncsfx47D+YCo6MbAcJBFUkIso/xYfZ62nfpxc3A68A1sdOHaNALx4kOVRsKmfTXk0HB13HZF6ddoJez0IYW3BdjVrZwtimCfQNlEBgHWXZEuHnVnxc+0sH55wV4rNfivH5T7YQh3MRujZqMVOwWZOOQkGJpkrQ+h0a7E59e76PWSQg8XFMBD9iT9OUEsB1PdEePa8OTSGjDb4wQYi8skaiJ6VtSS2Sqxocj11vwIAzQc7jxjzX9hkJohVt6xoTnRpa41PoK9URJVOozukgi0dRqdNvGUNHLWxtlZGmNhxtoTI4hZI/B2PrU9QqTTTmDKL5HIGTWhgaUbDKLWxZvVDwkKuNDJPdVWj0z0Vr0kK14qNabkMlGknkw2UIoxpHFh+Ibe1JWE6ItDsEWym41Sac2jaEUy3UK0egM1VGL26hUe9D2QcSFm0IqkgtViMnShydty62PBeaJBJdQUxg/1qCRi2Ab9XQjaZEy2bKtZNVMHe/CMce/SNMbbZw/zMfgLa4de/DuvWbUbbnCq51pdYDaiGaW/ow2Gihv9GPNZsCJEkGTytUBnwC7yEMiA3DR6/R63XQN0jnWgOl2EJU3iSIckEUorWpDT+bC38gRKkvQTLuSW1Jb+Ax8FmNbzxKClbZdQtlpp6vzTA82oPrsaDCAUC3itjfhC1TMSYnDsSxh1+KI179+7jlwaNh2ZtRtapI9Bqo3gFIwz5YFTqh6RsoYesmC0MDDraPd+FYFbh+C/1z6wiCSCADiFfW3A5MTLThej00KsNw5/RQYbSPoBOOI2gPYmJbD8oqE6oEwyMaRP6dasYINOF9LQzPGYelWtgSAHV/HoiwONUMEHaIeDeJgYFBlKpttIMWvHIfmt0UrTEX/SmzbhMklSmMjLrQ3SqcMn0sMZhubhGcnC4om4UGmPHq7UEFpV3XxL5vdp2B/z9GbdRbZIr53Q0kEeFKbcRxJtmJng9YDkGJ+pDqDFE3hkcAGEXDK8GEbDyyYjveeNwdKGMJSpVhXPCNHs48i2hym6Ct7XCzg+FmCwXcnEO3iNfL17SpwXx84XcKEIUwSKAzgulrAaizLaLN2YiI25sbkIxmDMH/IMiSrB4i6/GWAri06524+CW2XFLI845ZdABOIkibgmPhEYAHrKDCzM1AvOmEUQ1CG77fFdS7OLWIQyUAeuT3iQ7hux6irI2S4ia1ixgrJVoG0RFU+6CcJlRUgVVS6BIrNK6gWtbIQlZqH5ZrKC9GO3oWnrcd3biBsnsgVFqFazGtYrtUEiGUKUMJiTDYaYcoN5pQNsGf5oEAT3G2GbaeL8+Xk8/nSuxsr+Sjg22w0irKitE8QDvqiv3WcZuo4DAcueBrWL92HJujrxhNkBswa1LQFlmkIYsyZPVJpDpCVdWR9nxhUI4N2CEDdSLBQKEgZcUa164hinuy7a+XFqLTDFAevBdB3EBFHQNlTSDFeuhkCEFnLqpVG1ZpAs1WG7XakJQoC5IYrsdSaCEGMw+Tkwkaww7CSQiyI8H9Fw7eiCh8C3pYgdPe9zQu+/674CZlAV0q2dzd1ASOVVfo0O3Ajg6BykoCRZqFhMklDscUdG0TSnYDrhpF2JuEV+UmmJCybWThIWjrcXjeJBxdE1gBG/tDoV/msBtVkZaeRJ11ZZISonQUyi7DVzE6XQ23TpAlKhg12KmFkr8VnakY1fqojKnSIFYMq/wQ+LUB9AhgZiNlWr5idL6HlAAifCQZK8nTVm0YtYnVK4uCJF/ue3tJM/AK2ah36mPOpMX8QUdNGsL1PKRJKlqv6zjo9qZQa9jo6W0oOXVol1qSQqVm4jPT0EYYN3HcMQuRdYfxtvfGOOvsOhIVIg5jVNy5iK22oJJxkUqECSFAKfHJUYV58vjC1h9iXzA6xCu50JowrMbpl7AAeUATRcbcBWHkHsulsJYAzSxih6T2nqvJO01B8VHSbyX1loydhQd4ClHHyrAzwswzFI2VN7ZAqRIsRSddAq9UgkpD2brTWeZkLqJeAs/1ESQBPL+JTmdEojdYkIYIqISJVVEdTuwjYxUdJ4ZtV9HpNeH4rP1IbJIWVClC1ApRKntIMkvMSxpDcBXrYEew7Zpos5ZNkKoGmpMB/HJFGGK5QqQ3oMukIqcJmyXUnAYkGNiZQBzTNltG5rQFa63X0xgopwKkT9STqu9LCrdKGkgCC69fPBcLDqoiTqZQ8WpigsqyGmwGgLFQek0h0DY8y0fUCwThL80UgnAKVctHFpaQlQKkiS+OUr9agu8woiVFEARw6hzDIMosN4UUqbKRqDlw1CjqvoXUCdGLN6NR571bcEoevLSETmcTBqpVIPDQGHGQ6A5iZcMOHTiZgyVLRrFx6+N47aLX4htXHSbaaqnM3aIt1Vwovy0/QsQiGZiE7wVi9w10F1o1oOMI1UFWZiFIFkHBJuCVB2TX1ZpqospsJsdF3R6UCjtEtnPgI4wi2DSlSZSSRgD6IXzYjgWf+CZ8xUp2T4zipN+v7FjoJi1YhL31iL+Sol6zkLJyuUOH71qBFXa9uhRvCHQPZS6biMh5ZPak0V2jmuh52iudqFgU+467zMArr1FTbyq0afqbk7JoD3R2OQK6y6RFanRatq/a6cHVBFMqylNxWx1LdRZmp/UwhkyPomI1EUUVRCyqihzH2CvDETB/w5xnatQMzDdx0y/MqHkHVlihFk/bK4seJGkPJd5cYEMD0CGoUwLolKATg3VgFGjawQ2p/k6NmkLEIlunlmUJgJCcQYZPqCEJU2P9QkuUFC7CLDbCwPY1Esaeo2nQ8ji3MQF22qKlpRiVRUsRwFFaapsIATCMSkCf+F0kTjDq8E5GzWsdbLcJ9A5Fkvhw6sSQYHmmuYA7iVitRhIOo5TOk2ogEqtHByrZHPHEbfobQsEzTllCSu5cR0jENYodXZGsQjjLgHQYmToY0NwqE0czQBLMh+NvBeJhiH+5LjJWGCajYWRuKPuyntRipMRIbYWElVuUDx1p6BLbNWEn1Cw14jiCS3Q69EvZLZY006QRFifObMRJA47jyrwKLCfD4dU2pBbhZefD1g2zcESWZgjjAJb3KGw9D1Y2ioiVDVP6LoCM+TI+HWoKkd2BrRUsXRGhGPXosG1A9yxkpZ6ASbGMm9L9Is/JVrV6Fg7rbCYHI6FtKE4EJMtwX6JVASxMw66QQjzNyCfqB4lE+FgOs2n5m0lgUsqCI9J/CmnMCj5d2PTPhBUkrgU31yMybzMiwd+ZB0uncFi5KCB2KiN3CK5I57SUOUJiRdCowtZE5DPPlfVLJf6RTRyi6bET9X2M2lDOS35/+Rk1CUEIxxCWmANkk0sOxRgxE6fMwnS0/05roMLguGIIwE7G3TKcmhVMMgL+5NswLibJavSRRdQeFJRLMHoyD5ba6pCekbGeISMsSOn0Tk/3aTdzOK0Q0zFo4ruLM6iZTzNgia7gRcnUiWxmWv0Oi0dxiRc8im09tKDKWwFuPYl1bW8Rm6ZK92M5a+NhF+M2x2ucl8QBpmDRZBJUpznTtBnKH6eYfdyB1ie/881i+gdNKyx6wGswKqNu0oPpV2CKe8ZswJ4ISuh+pFzQLvsXQmUjsFgdRYUCJ6s1U+qLiebmLReKxeTQL1HaSBcqIHUvTZFg7iA4r9yZEEaAAtwUI24LcxI4XPZXs7Lujjh42SnJc+CPpiBDcU+GgxrBPP1A2YgWa1iMYyc6RrHzshmwxnFGEvVjninDFn3Bik6phtosGsEdygKpvg6rbcqw6aqBALBYPWU7UvpGwKriJjJCsyoPCxfQyWq5yAgRympDMiciToV+pHMUv5Fr6n2SIdJTyWxXVTYx23RM0j6csYqOoWn6NlgejvjqEl0lw2VhCnLbnqlQlAyaEnJ2KNCssubYOFcqjNmC1ZdIClT9OZ2EXKDfhMKCzziFgHO7zH/I/St81IKtTqWDfiGeaMwhZjz73l/sDLz8po9inUoPDYOQf/PFmpGwckYjBFMwaGEwxs1Ge6+mVkeiZmogNexsSLTDzGKs8jxkug+ZNiFyjAMmkRgwfsMchIEL4ZjPezVhZFKkWnnNPJ/qgxhN89/IaMy/QvezeULeZs8O0+YZvysqtA5tqQSCyjrJNKRwgp4vzMzsFNgvUymGUytrhp3QmRRtNdOab0VpjqFWJSYXaSkp/VJRm8xDtHdez2QACtMTrz4ZKAuZGnB4zomiIRhkoIlBQ5QxezIvZpEb3c+MmvNFbZOLnkKFMeOcM0+YoMwsGYCUp6KQKCFzx5DF+0sxXu3Qxh7DYXidtpFaz8KGKcxQzFcReiCfObSCzqRfBve7eAJso8hkCPjMsaR5KJmihs8ohoYIRkmFJnNk+bWMRhf2eRCgE0+R+dF5F0j5NdKDVPxhUQp0YUkhXI6M9rAYFhkr6UkGm0cFCcSueQ47NAjpPKzyWmTpAdLPzNqCjIIpPAiZpQF3NSD1HntQhKoln1SuOPwMHbKfpFs+G8NMaQeXCj7F/SWMLk9SEfBZXsWY8CTMjnYs7koTB2nKnQpD8tj/LuC2AMyXdSZmTNIbXyIsd2/2M433ve/JDLz8jFq0Z3ZFKMP0SXgetQjSPovC8sUv2YYERQ0nB3VxSGS0S9O+xt9dAbmnBkcCtIhHTI2CWpjDKtptSG0u5UlrqWNItkWNY5qA8lvuySGPUCFetXkV42D/yel2msKcVtmWZ8z4uCd327UN97EM5Fa0DbM4wahxFiXDQKKgWDk1v5MsRrkCzSQUILbYKgUg27KFLZrlT9MRU/PJMNlHMlbuUIyNnCn7PN8wHLbRRsOW8dCzT+ZLs04KlVSgXIYKknkXmlUAxagV7sOFwbFTfL5U0Sj0GLVDrZXaILXUiqQ3WzYZQEfGasxjZIlzzGOnQKSJgHORP0c7Y61CPo98puVZkTfnDJC3nfUACjuq0WCF/kTQMoY/E6FHXqNkUng/0pwpPWXU0wQZHdLyOxluDJU1oPiMJIOVTN8wPZoGeHNb6I7INqTVJI8v5vyxj6Rhzn0hQNhZ82cED59ojMxihXCDhcNoDtorOHeZvywauwAACG9JREFUXRGznJLCEdxZlKTvShG/hkNiNAjHzJ0XaYjPh17WcdnxiODh+CgoOSZq3BReMhZCENCsUTUOfLuda9wcH9cnfTc1IS2zg+JOLM/k5fqV52zK2O1K1Pu+2dsZ2InL7O3pe9q+YG55+5zWZy8iEhEJloQlVCaheoLzQWJmKSYeeSkyBcWtaQgVvkqsGSDjcbYhA1NluUgGZTstdxR10mgGcj5pmAyKt9nti2FGIlkMEQuxsxPSkRlnF4yc7QtNiT8X2sqMpnvzLyMF3BosGhPllvOMPSC1BY9DzBhyPQ6GGjAXMSdYDJdS5sts+QvtmI3ZQyKdsbo1ZzsXODRjyFyxDb/jroTzzLHlpCJzxs+xYY8pGfeQ2JdNm8gIS9YoYUQAGdWMl8ylEkgu019hdGzHPzJx9oEMnIyQUQesPsMuO7DThQIbQgeWdFzPz01l5vmYMZtQzIJZS8HVwrQgYy36IxfNixsbIb6jbFQuVCiw0j5DjnQOUPMWwznNdBR0CSxrwJgRuFkwj8AMgbQqf1RE8v5xHsSgTEYmnZEhGzos6MkQJZ8ZmWaGESk6K1AM2YhRYCwqLqwktEg0etYR1SlNL0wG12IqFHu+MGkJxzFCQeaWJpimmATplJWllj9bsW2TwbL0nYTXsUBxSSAdMu4YStz9MgOxX4QrM08dKTwRiUlF0byjfLObFfqbITBn0MC+f/d+Bgqq3fsz9/gMEiBfOfFTM+X2lpyS34mZgkQ7U/qyTe4uYchPvmMWfUgMZ8TpIEIcK1tkyFRLCE8nLnRCVDnCplKLoJYoupLxSu9YiXmf9u5gGB7PMYvKLClqhgVB5mPKNTtz9T1j1KLJPJ/k4E2o4YgpiPfZIkVToapiGtB6EJrlWPiSJCIOmIzQJPQoV9i2MFU2YUsuf8N82XcubW20SPmNzJlkwRsriefmv7Qjc9tvrmGKPjCWXTYUFJIy0QXDJJOiNm0LkzEczGj5vEZG80DB+GUrThApZoqwAzyXaModMaukapVo1QzFYz/EvJW4sAjWZU+KyUs6xTeZ95zeCrLjSWLqoeBifyiWjDLAnYIwfO7WaAcXHwnjmnkxaqwTUOkIwK0+aTZnuLTfE1FROsRq7Sm1S/pAWIyYJiXjXGUUCzFKxIwifaAm6pjvKMTkefO6hXLC+8ok5DZrOq9pbsmFhQolJyBjVXh4JG8omzROkyBt02aNESqBmwr+KX9DHstMAcjge5qZBsR/kyX9yJw4T1rhaDj33MFybLyWBzo48wtJ8WbR0hmKR9MQ50VexZEfZMHxfVpZMG32vb+UGXgFGPXO3eNDZSHXgllUDBMRWxppl0w6ZzzcrHMHKjtmbsGJ0kANwTgGbYH3ZGXwrmg4Ku2HpU1Eg1AKL0NmkmtwhrGa/hhTxp6EDxV28rxPYr81VxJZQ+aYD1EW/qzhziTgWT8874edmbUIBqdjFq8wQnrbjcnC4lZWtsOMtjDOIjrFRBbRNJ0n+bD37DlHWswqPxk7Kxc5W5AMcs4mih4db+yiGZnMEi9MhZdmEGp6ZHhMOpLEHDY1yUqyJc7oI+CCJRIiK10XXJNHtuMxnzV2SqImOR7jBGVHLdHoOF7aeU0dx8wyNnhFB6UIjSJyh4yOc80L8cjrzxytuR37NDOMzPSRAoXjpybK3QDPZ9/4l0AzHZqzJ+oyv6Njk0KLjmqm449DMWaTld3l+qQHjoUtSed1KMV+8jJUMHJBlXfXONGLuWYj007aCoHR70LfgHG2Z2TWFquJc644TI7Zl/lihIs8K02zoSf81VAvr5s/Y7lFBUoEC0to5c9G5Bk1Yi42NiJj9+WxiqkDvjhLxUSWyxgTpMV7krET0ZJHeZg7xmH+2/f+Emfg5Y/6kA6SKosX/89VZH4lkRjFb8WxaM9jbscrCJOajUjynPhkPVKjC/MtKL3iZttveMHMEDmhUkPfspjozNqBBV3cfeaxWO6F1iwLoWBq0rDoK0dlFujM83d3/aItmR9fwjyKL8k+Mpon8sWUfy+2cSbICDPcMZ2zmgnD4Qnsk5mrWWPJjJ2ZgtEsMCOzxWTAc0Ug0bzCiBu28XPzBPkgnYe8b1lsp7P7bHZCpgHnhJ8NY1Yz5np6vOyb7J5y27P4J3iKibzQyjgci/vn8jwX6CJVilvt9VEEIxmw0BPHzDnInZmyIyEzpKZNBsRpZKYm6ZHMyWj+hGXi6IzgM8+JfRStWoRNCbYIc5pzCMOYX0tOync9whjNLYp5MZ94ChtyDmmlprDl2uGOhsyYPojiqVKAG+B+xV0JY/iyFJlLfwOFLNdFPtdiN+e1e1AZmbNZF4ZWeGezczJzwd+KXSOZN3cF+SlyGn0TeR/kOnRy59eTvpk5Kcaz7/jiZsCszhd37h6eVSxcoUyz2Isz+Xxlkea/8Xt55vnDVQTETyS6Qx4933IbtmyB+VHMKC4U7Wo8TZiMuQHDq6gtklBnLwAyV/4VRF506IWOvHjBlHnM+zh9JDssvnuh6+z6W9G32QzPtJNwt6KbOf1Pr6sZl5r1HReOtBWOMd1XtuGlhKnIPBbzkGtnXMfFbiY/MozMMAou/OKGZFKcPhZI5TZ/JhlxDnbMww4TR37u9BjIcgwDMFouIUMN45WxsH8ZzTvmJQyfPwsPMcLHmFDyC+btpg/TfSVNTH876x+ONdMUvWRyNKtwHLbRkPkFzXIS1WIEl2FgbMN5Y/94bfbb+DG4O+FPvJ04YomjIq3pCDRx8NM8UAQpmWg+l3nPZj3H/E4i7Mmfi6EUQlgecyQFesWRWOyO2JATZ3Nc7K882HweOHfmeXNnYJi0oesC/a7og0T/5Hc1zzF/zkJHvAkrKvI7w7ynOzhtJioYfj64fYcXPQP/B8l1bmBYQM/ZAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Feature Space to target correlation is desired\n",
    "- Feature to feature correlation is not desired\n",
    "- If 2 features are highly correlated then either feature is redundant\n",
    "- Correlation in feature space increases model complexity\n",
    "- Removing correlated features improves model performance\n",
    "- Different model shows different performance over the correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrmat = X_train_unique.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18b5bd884a8>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(corrmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_correlation(data, threshold):\n",
    "    corr_col = set()\n",
    "    corrmat = data.corr()\n",
    "    for i in range(len(corrmat.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corrmat.iloc[i, j])> threshold:\n",
    "                colname = corrmat.columns[i]\n",
    "                corr_col.add(colname)\n",
    "    return corr_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{5,\n",
       " 7,\n",
       " 9,\n",
       " 11,\n",
       " 12,\n",
       " 14,\n",
       " 15,\n",
       " 16,\n",
       " 17,\n",
       " 18,\n",
       " 23,\n",
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       " 30,\n",
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       " 53,\n",
       " 54,\n",
       " 55,\n",
       " 56,\n",
       " 57,\n",
       " 58,\n",
       " 60,\n",
       " 61,\n",
       " 62,\n",
       " 65,\n",
       " 67,\n",
       " 68,\n",
       " 69,\n",
       " 70,\n",
       " 72,\n",
       " 76,\n",
       " 80,\n",
       " 81,\n",
       " 82,\n",
       " 83,\n",
       " 84,\n",
       " 86,\n",
       " 87,\n",
       " 88,\n",
       " 91,\n",
       " 93,\n",
       " 95,\n",
       " 98,\n",
       " 100,\n",
       " 101,\n",
       " 103,\n",
       " 104,\n",
       " 111,\n",
       " 115,\n",
       " 117,\n",
       " 120,\n",
       " 121,\n",
       " 125,\n",
       " 136,\n",
       " 138,\n",
       " 143,\n",
       " 146,\n",
       " 149,\n",
       " 153,\n",
       " 154,\n",
       " 157,\n",
       " 158,\n",
       " 161,\n",
       " 162,\n",
       " 163,\n",
       " 164,\n",
       " 169,\n",
       " 170,\n",
       " 173,\n",
       " 180,\n",
       " 182,\n",
       " 183,\n",
       " 184,\n",
       " 185,\n",
       " 188,\n",
       " 189,\n",
       " 190,\n",
       " 191,\n",
       " 192,\n",
       " 193,\n",
       " 194,\n",
       " 195,\n",
       " 197,\n",
       " 198,\n",
       " 199,\n",
       " 204,\n",
       " 205,\n",
       " 207,\n",
       " 208,\n",
       " 215,\n",
       " 216,\n",
       " 217,\n",
       " 219,\n",
       " 220,\n",
       " 221,\n",
       " 223,\n",
       " 224,\n",
       " 227,\n",
       " 228,\n",
       " 229,\n",
       " 230,\n",
       " 231,\n",
       " 232,\n",
       " 234,\n",
       " 235,\n",
       " 236,\n",
       " 237,\n",
       " 238,\n",
       " 239,\n",
       " 240,\n",
       " 241,\n",
       " 242,\n",
       " 243}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features = get_correlation(X_train_unique, 0.85)\n",
    "corr_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corr_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_uncorr = X_train_unique.drop(labels=corr_features, axis = 1)\n",
    "X_test_uncorr = X_test_unique.drop(labels = corr_features, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16000, 103), (4000, 103))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_uncorr.shape, X_test_uncorr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.95875\n",
      "Wall time: 912 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train_uncorr, X_test_uncorr, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.9585\n",
      "Wall time: 1.53 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.3921568627451"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1.53-0.912)*100/1.53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Grouping and Feature Importance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>235</th>\n",
       "      <th>236</th>\n",
       "      <th>237</th>\n",
       "      <th>238</th>\n",
       "      <th>239</th>\n",
       "      <th>240</th>\n",
       "      <th>241</th>\n",
       "      <th>242</th>\n",
       "      <th>243</th>\n",
       "      <th>244</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.025277</td>\n",
       "      <td>-0.001942</td>\n",
       "      <td>0.003594</td>\n",
       "      <td>0.004054</td>\n",
       "      <td>-0.001697</td>\n",
       "      <td>-0.015882</td>\n",
       "      <td>-0.019807</td>\n",
       "      <td>0.000956</td>\n",
       "      <td>-0.000588</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001337</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>-0.008500</td>\n",
       "      <td>0.006554</td>\n",
       "      <td>0.005907</td>\n",
       "      <td>0.008825</td>\n",
       "      <td>-0.009174</td>\n",
       "      <td>0.012031</td>\n",
       "      <td>0.012128</td>\n",
       "      <td>0.006612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.025277</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007647</td>\n",
       "      <td>0.001819</td>\n",
       "      <td>0.008981</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.001638</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>0.000695</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>0.000922</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>0.000415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.001942</td>\n",
       "      <td>-0.007647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030919</td>\n",
       "      <td>0.106245</td>\n",
       "      <td>0.109140</td>\n",
       "      <td>0.048524</td>\n",
       "      <td>0.055708</td>\n",
       "      <td>0.004040</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025522</td>\n",
       "      <td>0.020168</td>\n",
       "      <td>0.011550</td>\n",
       "      <td>0.019325</td>\n",
       "      <td>0.019527</td>\n",
       "      <td>0.041321</td>\n",
       "      <td>0.016172</td>\n",
       "      <td>0.043577</td>\n",
       "      <td>0.044281</td>\n",
       "      <td>-0.000810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003594</td>\n",
       "      <td>0.001819</td>\n",
       "      <td>0.030919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029418</td>\n",
       "      <td>0.024905</td>\n",
       "      <td>0.014513</td>\n",
       "      <td>0.013857</td>\n",
       "      <td>-0.000613</td>\n",
       "      <td>-0.000691</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014032</td>\n",
       "      <td>-0.000583</td>\n",
       "      <td>-0.000337</td>\n",
       "      <td>-0.000548</td>\n",
       "      <td>-0.000561</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>-0.000577</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004054</td>\n",
       "      <td>0.008981</td>\n",
       "      <td>0.106245</td>\n",
       "      <td>0.029418</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.888789</td>\n",
       "      <td>0.381632</td>\n",
       "      <td>0.341266</td>\n",
       "      <td>0.012927</td>\n",
       "      <td>0.019674</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002328</td>\n",
       "      <td>0.016743</td>\n",
       "      <td>-0.001662</td>\n",
       "      <td>0.020509</td>\n",
       "      <td>0.021276</td>\n",
       "      <td>-0.001905</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>-0.002552</td>\n",
       "      <td>-0.002736</td>\n",
       "      <td>0.003656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.001697</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.109140</td>\n",
       "      <td>0.024905</td>\n",
       "      <td>0.888789</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.363680</td>\n",
       "      <td>0.384820</td>\n",
       "      <td>0.017671</td>\n",
       "      <td>0.030060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.010860</td>\n",
       "      <td>-0.001706</td>\n",
       "      <td>0.012963</td>\n",
       "      <td>0.013553</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.007096</td>\n",
       "      <td>-0.001672</td>\n",
       "      <td>-0.001844</td>\n",
       "      <td>0.002257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.015882</td>\n",
       "      <td>0.001638</td>\n",
       "      <td>0.048524</td>\n",
       "      <td>0.014513</td>\n",
       "      <td>0.381632</td>\n",
       "      <td>0.363680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.908158</td>\n",
       "      <td>0.030397</td>\n",
       "      <td>0.036359</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000485</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>-0.000301</td>\n",
       "      <td>0.002590</td>\n",
       "      <td>0.003867</td>\n",
       "      <td>-0.000818</td>\n",
       "      <td>-0.000515</td>\n",
       "      <td>-0.000779</td>\n",
       "      <td>-0.000839</td>\n",
       "      <td>0.004448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.019807</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.055708</td>\n",
       "      <td>0.013857</td>\n",
       "      <td>0.341266</td>\n",
       "      <td>0.384820</td>\n",
       "      <td>0.908158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047667</td>\n",
       "      <td>0.056456</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000514</td>\n",
       "      <td>0.006336</td>\n",
       "      <td>-0.000318</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>0.003707</td>\n",
       "      <td>-0.000866</td>\n",
       "      <td>-0.000545</td>\n",
       "      <td>-0.000825</td>\n",
       "      <td>-0.000888</td>\n",
       "      <td>0.002427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000956</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>0.004040</td>\n",
       "      <td>-0.000613</td>\n",
       "      <td>0.012927</td>\n",
       "      <td>0.017671</td>\n",
       "      <td>0.030397</td>\n",
       "      <td>0.047667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988256</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000184</td>\n",
       "      <td>-0.000197</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000185</td>\n",
       "      <td>-0.000189</td>\n",
       "      <td>-0.000309</td>\n",
       "      <td>-0.000195</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-0.000739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.000588</td>\n",
       "      <td>0.000695</td>\n",
       "      <td>0.005796</td>\n",
       "      <td>-0.000691</td>\n",
       "      <td>0.019674</td>\n",
       "      <td>0.030060</td>\n",
       "      <td>0.036359</td>\n",
       "      <td>0.056456</td>\n",
       "      <td>0.988256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>-0.000222</td>\n",
       "      <td>-0.000128</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>-0.000220</td>\n",
       "      <td>-0.000332</td>\n",
       "      <td>-0.000358</td>\n",
       "      <td>-0.000811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.012443</td>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.042368</td>\n",
       "      <td>0.012451</td>\n",
       "      <td>0.298916</td>\n",
       "      <td>0.280081</td>\n",
       "      <td>0.805265</td>\n",
       "      <td>0.706608</td>\n",
       "      <td>0.388309</td>\n",
       "      <td>0.398826</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000452</td>\n",
       "      <td>-0.000485</td>\n",
       "      <td>-0.000280</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>-0.000100</td>\n",
       "      <td>-0.000762</td>\n",
       "      <td>-0.000480</td>\n",
       "      <td>-0.000726</td>\n",
       "      <td>-0.000782</td>\n",
       "      <td>0.003341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.010319</td>\n",
       "      <td>0.009097</td>\n",
       "      <td>0.096719</td>\n",
       "      <td>0.026377</td>\n",
       "      <td>0.938409</td>\n",
       "      <td>0.824893</td>\n",
       "      <td>0.038751</td>\n",
       "      <td>0.029445</td>\n",
       "      <td>0.002612</td>\n",
       "      <td>0.007677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002699</td>\n",
       "      <td>0.015727</td>\n",
       "      <td>-0.001684</td>\n",
       "      <td>0.021203</td>\n",
       "      <td>0.021555</td>\n",
       "      <td>-0.001754</td>\n",
       "      <td>-0.000494</td>\n",
       "      <td>-0.002467</td>\n",
       "      <td>-0.002645</td>\n",
       "      <td>0.002290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.005268</td>\n",
       "      <td>0.009360</td>\n",
       "      <td>0.098070</td>\n",
       "      <td>0.021968</td>\n",
       "      <td>0.838953</td>\n",
       "      <td>0.943622</td>\n",
       "      <td>0.067664</td>\n",
       "      <td>0.057591</td>\n",
       "      <td>0.002018</td>\n",
       "      <td>0.012266</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000540</td>\n",
       "      <td>0.009474</td>\n",
       "      <td>-0.001732</td>\n",
       "      <td>0.013133</td>\n",
       "      <td>0.013330</td>\n",
       "      <td>0.001253</td>\n",
       "      <td>0.007871</td>\n",
       "      <td>-0.001513</td>\n",
       "      <td>-0.001676</td>\n",
       "      <td>0.001570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.017605</td>\n",
       "      <td>-0.002511</td>\n",
       "      <td>0.082025</td>\n",
       "      <td>0.016331</td>\n",
       "      <td>0.266746</td>\n",
       "      <td>0.254702</td>\n",
       "      <td>0.040788</td>\n",
       "      <td>0.033996</td>\n",
       "      <td>0.108329</td>\n",
       "      <td>0.106806</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001670</td>\n",
       "      <td>0.034002</td>\n",
       "      <td>-0.001035</td>\n",
       "      <td>0.038103</td>\n",
       "      <td>0.038047</td>\n",
       "      <td>0.007400</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.006688</td>\n",
       "      <td>0.006283</td>\n",
       "      <td>0.000707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.016960</td>\n",
       "      <td>-0.001086</td>\n",
       "      <td>0.095485</td>\n",
       "      <td>0.016458</td>\n",
       "      <td>0.326051</td>\n",
       "      <td>0.359897</td>\n",
       "      <td>0.048914</td>\n",
       "      <td>0.045136</td>\n",
       "      <td>0.081030</td>\n",
       "      <td>0.081962</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002040</td>\n",
       "      <td>0.025566</td>\n",
       "      <td>-0.001264</td>\n",
       "      <td>0.028641</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>0.004485</td>\n",
       "      <td>0.001176</td>\n",
       "      <td>0.004012</td>\n",
       "      <td>0.003599</td>\n",
       "      <td>-0.001992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.018040</td>\n",
       "      <td>0.002426</td>\n",
       "      <td>0.106415</td>\n",
       "      <td>0.024014</td>\n",
       "      <td>0.638412</td>\n",
       "      <td>0.565620</td>\n",
       "      <td>0.043920</td>\n",
       "      <td>0.033716</td>\n",
       "      <td>0.083438</td>\n",
       "      <td>0.084688</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.033265</td>\n",
       "      <td>-0.001589</td>\n",
       "      <td>0.038978</td>\n",
       "      <td>0.039103</td>\n",
       "      <td>0.004773</td>\n",
       "      <td>0.001465</td>\n",
       "      <td>0.003984</td>\n",
       "      <td>0.003632</td>\n",
       "      <td>0.001339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.017400</td>\n",
       "      <td>-0.002401</td>\n",
       "      <td>0.081028</td>\n",
       "      <td>0.015979</td>\n",
       "      <td>0.263482</td>\n",
       "      <td>0.252160</td>\n",
       "      <td>0.043357</td>\n",
       "      <td>0.038548</td>\n",
       "      <td>0.214397</td>\n",
       "      <td>0.211633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001661</td>\n",
       "      <td>0.033386</td>\n",
       "      <td>-0.001029</td>\n",
       "      <td>0.037417</td>\n",
       "      <td>0.037362</td>\n",
       "      <td>0.007237</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>0.006539</td>\n",
       "      <td>0.006139</td>\n",
       "      <td>0.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.016745</td>\n",
       "      <td>-0.001019</td>\n",
       "      <td>0.095009</td>\n",
       "      <td>0.016239</td>\n",
       "      <td>0.324417</td>\n",
       "      <td>0.358769</td>\n",
       "      <td>0.051373</td>\n",
       "      <td>0.049260</td>\n",
       "      <td>0.160240</td>\n",
       "      <td>0.162113</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002037</td>\n",
       "      <td>0.025295</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>0.028341</td>\n",
       "      <td>0.028312</td>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.001147</td>\n",
       "      <td>0.003946</td>\n",
       "      <td>0.003534</td>\n",
       "      <td>-0.002038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.015206</td>\n",
       "      <td>0.002629</td>\n",
       "      <td>0.110912</td>\n",
       "      <td>0.025558</td>\n",
       "      <td>0.673593</td>\n",
       "      <td>0.599584</td>\n",
       "      <td>0.190138</td>\n",
       "      <td>0.162168</td>\n",
       "      <td>0.152035</td>\n",
       "      <td>0.155173</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000074</td>\n",
       "      <td>0.032155</td>\n",
       "      <td>-0.001591</td>\n",
       "      <td>0.037742</td>\n",
       "      <td>0.037884</td>\n",
       "      <td>0.004487</td>\n",
       "      <td>0.001333</td>\n",
       "      <td>0.003729</td>\n",
       "      <td>0.003377</td>\n",
       "      <td>0.001910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.000103</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.016886</td>\n",
       "      <td>-0.000520</td>\n",
       "      <td>0.049579</td>\n",
       "      <td>0.042621</td>\n",
       "      <td>0.012454</td>\n",
       "      <td>0.007797</td>\n",
       "      <td>-0.000175</td>\n",
       "      <td>-0.000198</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000156</td>\n",
       "      <td>-0.000167</td>\n",
       "      <td>-0.000096</td>\n",
       "      <td>-0.000156</td>\n",
       "      <td>-0.000160</td>\n",
       "      <td>-0.000262</td>\n",
       "      <td>-0.000165</td>\n",
       "      <td>-0.000250</td>\n",
       "      <td>-0.000269</td>\n",
       "      <td>0.000213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.001198</td>\n",
       "      <td>0.004590</td>\n",
       "      <td>0.107680</td>\n",
       "      <td>0.007478</td>\n",
       "      <td>0.227803</td>\n",
       "      <td>0.238159</td>\n",
       "      <td>0.306165</td>\n",
       "      <td>0.284353</td>\n",
       "      <td>0.125108</td>\n",
       "      <td>0.138077</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001365</td>\n",
       "      <td>-0.001462</td>\n",
       "      <td>-0.000846</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.001829</td>\n",
       "      <td>0.009008</td>\n",
       "      <td>-0.001449</td>\n",
       "      <td>0.009156</td>\n",
       "      <td>0.011164</td>\n",
       "      <td>-0.001227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.006814</td>\n",
       "      <td>-0.008975</td>\n",
       "      <td>-0.105502</td>\n",
       "      <td>-0.002101</td>\n",
       "      <td>-0.208030</td>\n",
       "      <td>-0.211873</td>\n",
       "      <td>-0.071459</td>\n",
       "      <td>-0.078593</td>\n",
       "      <td>-0.012763</td>\n",
       "      <td>-0.021318</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002674</td>\n",
       "      <td>-0.049920</td>\n",
       "      <td>-0.037729</td>\n",
       "      <td>-0.038529</td>\n",
       "      <td>-0.041438</td>\n",
       "      <td>-0.000421</td>\n",
       "      <td>-0.010257</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.002306</td>\n",
       "      <td>-0.016447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.002037</td>\n",
       "      <td>0.041015</td>\n",
       "      <td>-0.102487</td>\n",
       "      <td>0.017541</td>\n",
       "      <td>0.041167</td>\n",
       "      <td>0.041372</td>\n",
       "      <td>-0.006549</td>\n",
       "      <td>-0.008179</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>0.001088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009114</td>\n",
       "      <td>-0.012641</td>\n",
       "      <td>-0.011070</td>\n",
       "      <td>-0.008324</td>\n",
       "      <td>-0.009368</td>\n",
       "      <td>0.013263</td>\n",
       "      <td>0.004114</td>\n",
       "      <td>0.013717</td>\n",
       "      <td>0.014768</td>\n",
       "      <td>-0.056029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.010356</td>\n",
       "      <td>0.008019</td>\n",
       "      <td>0.107570</td>\n",
       "      <td>0.003429</td>\n",
       "      <td>0.200514</td>\n",
       "      <td>0.182937</td>\n",
       "      <td>0.035401</td>\n",
       "      <td>0.025512</td>\n",
       "      <td>0.012907</td>\n",
       "      <td>0.018006</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002401</td>\n",
       "      <td>0.031418</td>\n",
       "      <td>-0.001487</td>\n",
       "      <td>0.033291</td>\n",
       "      <td>0.034573</td>\n",
       "      <td>0.001397</td>\n",
       "      <td>0.011918</td>\n",
       "      <td>-0.001487</td>\n",
       "      <td>-0.001591</td>\n",
       "      <td>0.012346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.012021</td>\n",
       "      <td>0.007439</td>\n",
       "      <td>0.101605</td>\n",
       "      <td>0.004843</td>\n",
       "      <td>0.220673</td>\n",
       "      <td>0.201909</td>\n",
       "      <td>0.039018</td>\n",
       "      <td>0.028469</td>\n",
       "      <td>0.014240</td>\n",
       "      <td>0.019760</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002227</td>\n",
       "      <td>0.034079</td>\n",
       "      <td>-0.001380</td>\n",
       "      <td>0.036066</td>\n",
       "      <td>0.037450</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.013156</td>\n",
       "      <td>-0.001036</td>\n",
       "      <td>-0.001105</td>\n",
       "      <td>-0.003767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.011525</td>\n",
       "      <td>0.273152</td>\n",
       "      <td>0.010099</td>\n",
       "      <td>0.027387</td>\n",
       "      <td>0.026378</td>\n",
       "      <td>0.046258</td>\n",
       "      <td>0.033114</td>\n",
       "      <td>-0.003889</td>\n",
       "      <td>-0.004384</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003450</td>\n",
       "      <td>0.020817</td>\n",
       "      <td>-0.002138</td>\n",
       "      <td>0.022279</td>\n",
       "      <td>0.023163</td>\n",
       "      <td>-0.000543</td>\n",
       "      <td>-0.003662</td>\n",
       "      <td>-0.001555</td>\n",
       "      <td>-0.001463</td>\n",
       "      <td>0.012034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.001138</td>\n",
       "      <td>0.009467</td>\n",
       "      <td>0.231649</td>\n",
       "      <td>0.015117</td>\n",
       "      <td>0.033757</td>\n",
       "      <td>0.037053</td>\n",
       "      <td>0.044225</td>\n",
       "      <td>0.034049</td>\n",
       "      <td>-0.003194</td>\n",
       "      <td>-0.003600</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002834</td>\n",
       "      <td>0.026148</td>\n",
       "      <td>-0.001756</td>\n",
       "      <td>0.027806</td>\n",
       "      <td>0.028888</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>-0.003008</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.006643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.004836</td>\n",
       "      <td>0.009771</td>\n",
       "      <td>0.299165</td>\n",
       "      <td>0.036569</td>\n",
       "      <td>-0.010411</td>\n",
       "      <td>-0.013701</td>\n",
       "      <td>0.020327</td>\n",
       "      <td>0.019508</td>\n",
       "      <td>-0.003295</td>\n",
       "      <td>-0.003715</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002924</td>\n",
       "      <td>-0.003132</td>\n",
       "      <td>-0.001811</td>\n",
       "      <td>-0.001902</td>\n",
       "      <td>-0.001440</td>\n",
       "      <td>-0.003893</td>\n",
       "      <td>-0.003103</td>\n",
       "      <td>-0.003587</td>\n",
       "      <td>-0.003989</td>\n",
       "      <td>0.012240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.006480</td>\n",
       "      <td>0.008796</td>\n",
       "      <td>0.241707</td>\n",
       "      <td>0.040420</td>\n",
       "      <td>-0.012628</td>\n",
       "      <td>-0.018755</td>\n",
       "      <td>0.009992</td>\n",
       "      <td>0.003331</td>\n",
       "      <td>-0.002969</td>\n",
       "      <td>-0.003347</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002634</td>\n",
       "      <td>-0.002822</td>\n",
       "      <td>-0.001632</td>\n",
       "      <td>-0.001507</td>\n",
       "      <td>-0.000986</td>\n",
       "      <td>-0.004438</td>\n",
       "      <td>-0.002796</td>\n",
       "      <td>-0.004228</td>\n",
       "      <td>-0.004553</td>\n",
       "      <td>0.007400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.005811</td>\n",
       "      <td>0.008676</td>\n",
       "      <td>0.237830</td>\n",
       "      <td>0.041165</td>\n",
       "      <td>-0.012035</td>\n",
       "      <td>-0.018146</td>\n",
       "      <td>0.010326</td>\n",
       "      <td>0.003592</td>\n",
       "      <td>-0.002929</td>\n",
       "      <td>-0.003301</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002599</td>\n",
       "      <td>-0.002784</td>\n",
       "      <td>-0.001610</td>\n",
       "      <td>-0.001456</td>\n",
       "      <td>-0.000927</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.002758</td>\n",
       "      <td>-0.004171</td>\n",
       "      <td>-0.004491</td>\n",
       "      <td>0.006121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>0.006937</td>\n",
       "      <td>0.002152</td>\n",
       "      <td>0.043278</td>\n",
       "      <td>0.002314</td>\n",
       "      <td>0.073627</td>\n",
       "      <td>0.084908</td>\n",
       "      <td>0.009789</td>\n",
       "      <td>0.009653</td>\n",
       "      <td>0.000540</td>\n",
       "      <td>0.013546</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000644</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>-0.000399</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.003709</td>\n",
       "      <td>0.082936</td>\n",
       "      <td>0.230452</td>\n",
       "      <td>0.029915</td>\n",
       "      <td>0.033617</td>\n",
       "      <td>0.000338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>0.004924</td>\n",
       "      <td>0.002210</td>\n",
       "      <td>0.045622</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.086904</td>\n",
       "      <td>0.093401</td>\n",
       "      <td>0.042081</td>\n",
       "      <td>0.029906</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.012702</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000662</td>\n",
       "      <td>0.003721</td>\n",
       "      <td>-0.000410</td>\n",
       "      <td>0.004129</td>\n",
       "      <td>0.004359</td>\n",
       "      <td>0.074334</td>\n",
       "      <td>0.206808</td>\n",
       "      <td>0.026758</td>\n",
       "      <td>0.030066</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.003979</td>\n",
       "      <td>0.149586</td>\n",
       "      <td>0.001554</td>\n",
       "      <td>-0.003401</td>\n",
       "      <td>-0.004867</td>\n",
       "      <td>0.024589</td>\n",
       "      <td>0.017603</td>\n",
       "      <td>-0.001336</td>\n",
       "      <td>-0.001506</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001185</td>\n",
       "      <td>0.011108</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>0.011385</td>\n",
       "      <td>0.011627</td>\n",
       "      <td>-0.001886</td>\n",
       "      <td>-0.001258</td>\n",
       "      <td>-0.001828</td>\n",
       "      <td>-0.001966</td>\n",
       "      <td>0.017276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>-0.000582</td>\n",
       "      <td>0.002581</td>\n",
       "      <td>0.093124</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>-0.007050</td>\n",
       "      <td>-0.006547</td>\n",
       "      <td>-0.002282</td>\n",
       "      <td>-0.002111</td>\n",
       "      <td>-0.000865</td>\n",
       "      <td>-0.000976</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000768</td>\n",
       "      <td>0.012837</td>\n",
       "      <td>-0.000476</td>\n",
       "      <td>0.013076</td>\n",
       "      <td>0.013340</td>\n",
       "      <td>-0.001294</td>\n",
       "      <td>-0.000815</td>\n",
       "      <td>-0.001232</td>\n",
       "      <td>-0.001327</td>\n",
       "      <td>0.006644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>0.007130</td>\n",
       "      <td>0.004811</td>\n",
       "      <td>0.178546</td>\n",
       "      <td>0.002540</td>\n",
       "      <td>-0.002079</td>\n",
       "      <td>-0.004782</td>\n",
       "      <td>0.036168</td>\n",
       "      <td>0.025934</td>\n",
       "      <td>-0.001618</td>\n",
       "      <td>-0.001823</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001435</td>\n",
       "      <td>0.010157</td>\n",
       "      <td>-0.000889</td>\n",
       "      <td>0.010430</td>\n",
       "      <td>0.010646</td>\n",
       "      <td>-0.002222</td>\n",
       "      <td>-0.001523</td>\n",
       "      <td>-0.002103</td>\n",
       "      <td>-0.002237</td>\n",
       "      <td>0.018092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>0.007675</td>\n",
       "      <td>0.004879</td>\n",
       "      <td>0.179565</td>\n",
       "      <td>0.002948</td>\n",
       "      <td>-0.001151</td>\n",
       "      <td>-0.003808</td>\n",
       "      <td>0.038964</td>\n",
       "      <td>0.028107</td>\n",
       "      <td>-0.001640</td>\n",
       "      <td>-0.001849</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001455</td>\n",
       "      <td>0.009732</td>\n",
       "      <td>-0.000902</td>\n",
       "      <td>0.010011</td>\n",
       "      <td>0.010224</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>-0.001545</td>\n",
       "      <td>-0.002116</td>\n",
       "      <td>-0.002243</td>\n",
       "      <td>0.017579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>-0.006477</td>\n",
       "      <td>0.005759</td>\n",
       "      <td>0.178263</td>\n",
       "      <td>-0.005438</td>\n",
       "      <td>0.002963</td>\n",
       "      <td>-0.001631</td>\n",
       "      <td>0.048470</td>\n",
       "      <td>0.029154</td>\n",
       "      <td>-0.001943</td>\n",
       "      <td>-0.002190</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001724</td>\n",
       "      <td>-0.001846</td>\n",
       "      <td>-0.001068</td>\n",
       "      <td>-0.001729</td>\n",
       "      <td>-0.001768</td>\n",
       "      <td>-0.002904</td>\n",
       "      <td>-0.001829</td>\n",
       "      <td>-0.002766</td>\n",
       "      <td>-0.002979</td>\n",
       "      <td>0.014736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>-0.010219</td>\n",
       "      <td>0.003183</td>\n",
       "      <td>0.094741</td>\n",
       "      <td>-0.003083</td>\n",
       "      <td>0.040691</td>\n",
       "      <td>0.027749</td>\n",
       "      <td>0.133603</td>\n",
       "      <td>0.084716</td>\n",
       "      <td>-0.001073</td>\n",
       "      <td>-0.001210</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000952</td>\n",
       "      <td>-0.001020</td>\n",
       "      <td>-0.000590</td>\n",
       "      <td>-0.000958</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>-0.001604</td>\n",
       "      <td>-0.001011</td>\n",
       "      <td>-0.001528</td>\n",
       "      <td>-0.001646</td>\n",
       "      <td>0.002052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>-0.011386</td>\n",
       "      <td>0.006355</td>\n",
       "      <td>0.200415</td>\n",
       "      <td>0.025778</td>\n",
       "      <td>-0.000914</td>\n",
       "      <td>-0.005809</td>\n",
       "      <td>0.038767</td>\n",
       "      <td>0.022672</td>\n",
       "      <td>-0.002144</td>\n",
       "      <td>-0.002417</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001903</td>\n",
       "      <td>-0.002038</td>\n",
       "      <td>-0.001179</td>\n",
       "      <td>-0.001788</td>\n",
       "      <td>-0.001769</td>\n",
       "      <td>-0.003205</td>\n",
       "      <td>-0.002019</td>\n",
       "      <td>-0.003054</td>\n",
       "      <td>-0.003288</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>-0.011200</td>\n",
       "      <td>0.006248</td>\n",
       "      <td>0.195652</td>\n",
       "      <td>0.033042</td>\n",
       "      <td>-0.000322</td>\n",
       "      <td>-0.005729</td>\n",
       "      <td>0.043137</td>\n",
       "      <td>0.025504</td>\n",
       "      <td>-0.002108</td>\n",
       "      <td>-0.002376</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001871</td>\n",
       "      <td>-0.002004</td>\n",
       "      <td>-0.001159</td>\n",
       "      <td>-0.001801</td>\n",
       "      <td>-0.001804</td>\n",
       "      <td>-0.003151</td>\n",
       "      <td>-0.001985</td>\n",
       "      <td>-0.003002</td>\n",
       "      <td>-0.003233</td>\n",
       "      <td>0.014628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.006455</td>\n",
       "      <td>0.002629</td>\n",
       "      <td>0.125618</td>\n",
       "      <td>-0.001532</td>\n",
       "      <td>0.003267</td>\n",
       "      <td>0.004018</td>\n",
       "      <td>0.013532</td>\n",
       "      <td>0.021485</td>\n",
       "      <td>-0.000883</td>\n",
       "      <td>-0.000995</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000783</td>\n",
       "      <td>-0.000839</td>\n",
       "      <td>-0.000485</td>\n",
       "      <td>-0.000788</td>\n",
       "      <td>-0.000807</td>\n",
       "      <td>-0.001195</td>\n",
       "      <td>-0.000831</td>\n",
       "      <td>-0.001146</td>\n",
       "      <td>-0.001241</td>\n",
       "      <td>0.014567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.008361</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.059293</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>0.010896</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.002878</td>\n",
       "      <td>-0.000492</td>\n",
       "      <td>-0.000555</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000437</td>\n",
       "      <td>-0.000468</td>\n",
       "      <td>-0.000271</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>-0.000450</td>\n",
       "      <td>-0.000573</td>\n",
       "      <td>-0.000463</td>\n",
       "      <td>-0.000556</td>\n",
       "      <td>-0.000608</td>\n",
       "      <td>0.005688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.003765</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>0.135362</td>\n",
       "      <td>-0.001817</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.012108</td>\n",
       "      <td>0.019415</td>\n",
       "      <td>-0.000950</td>\n",
       "      <td>-0.001071</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000843</td>\n",
       "      <td>-0.000903</td>\n",
       "      <td>-0.000522</td>\n",
       "      <td>-0.000848</td>\n",
       "      <td>-0.000868</td>\n",
       "      <td>-0.001417</td>\n",
       "      <td>-0.000895</td>\n",
       "      <td>-0.001348</td>\n",
       "      <td>-0.001452</td>\n",
       "      <td>0.015351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.005352</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.132537</td>\n",
       "      <td>-0.001698</td>\n",
       "      <td>0.001272</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.010082</td>\n",
       "      <td>0.016429</td>\n",
       "      <td>-0.000930</td>\n",
       "      <td>-0.001048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000825</td>\n",
       "      <td>-0.000884</td>\n",
       "      <td>-0.000511</td>\n",
       "      <td>-0.000830</td>\n",
       "      <td>-0.000850</td>\n",
       "      <td>-0.001387</td>\n",
       "      <td>-0.000876</td>\n",
       "      <td>-0.001318</td>\n",
       "      <td>-0.001421</td>\n",
       "      <td>0.014485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.008042</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.023435</td>\n",
       "      <td>-0.000354</td>\n",
       "      <td>-0.001629</td>\n",
       "      <td>-0.001719</td>\n",
       "      <td>-0.000318</td>\n",
       "      <td>-0.000337</td>\n",
       "      <td>-0.000120</td>\n",
       "      <td>-0.000136</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>-0.000066</td>\n",
       "      <td>0.004306</td>\n",
       "      <td>0.004022</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>-0.000113</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.013197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>0.007870</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.022679</td>\n",
       "      <td>-0.000338</td>\n",
       "      <td>-0.001669</td>\n",
       "      <td>-0.001713</td>\n",
       "      <td>-0.000302</td>\n",
       "      <td>-0.000320</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000129</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>-0.000108</td>\n",
       "      <td>-0.000063</td>\n",
       "      <td>-0.000102</td>\n",
       "      <td>-0.000104</td>\n",
       "      <td>-0.000171</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>-0.000163</td>\n",
       "      <td>-0.000175</td>\n",
       "      <td>0.012842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>0.007952</td>\n",
       "      <td>0.000411</td>\n",
       "      <td>0.025362</td>\n",
       "      <td>-0.000285</td>\n",
       "      <td>-0.001677</td>\n",
       "      <td>-0.001846</td>\n",
       "      <td>-0.000365</td>\n",
       "      <td>-0.000387</td>\n",
       "      <td>-0.000138</td>\n",
       "      <td>-0.000156</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000123</td>\n",
       "      <td>0.007701</td>\n",
       "      <td>-0.000076</td>\n",
       "      <td>0.011513</td>\n",
       "      <td>0.010768</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>-0.000130</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.013321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>0.008021</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.025406</td>\n",
       "      <td>-0.000334</td>\n",
       "      <td>-0.001730</td>\n",
       "      <td>-0.001875</td>\n",
       "      <td>-0.000362</td>\n",
       "      <td>-0.000383</td>\n",
       "      <td>-0.000137</td>\n",
       "      <td>-0.000154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000121</td>\n",
       "      <td>0.006078</td>\n",
       "      <td>-0.000075</td>\n",
       "      <td>0.009101</td>\n",
       "      <td>0.008510</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>-0.000129</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.013418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>-0.001596</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>0.013612</td>\n",
       "      <td>-0.000391</td>\n",
       "      <td>-0.001930</td>\n",
       "      <td>-0.001981</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>-0.000370</td>\n",
       "      <td>-0.000132</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.538270</td>\n",
       "      <td>-0.000125</td>\n",
       "      <td>-0.000073</td>\n",
       "      <td>-0.000118</td>\n",
       "      <td>-0.000121</td>\n",
       "      <td>0.031970</td>\n",
       "      <td>-0.000124</td>\n",
       "      <td>0.068648</td>\n",
       "      <td>0.057673</td>\n",
       "      <td>-0.000203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>0.001830</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.023446</td>\n",
       "      <td>0.008469</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>-0.000407</td>\n",
       "      <td>-0.000404</td>\n",
       "      <td>-0.000428</td>\n",
       "      <td>-0.000153</td>\n",
       "      <td>-0.000172</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950232</td>\n",
       "      <td>-0.000145</td>\n",
       "      <td>-0.000084</td>\n",
       "      <td>-0.000136</td>\n",
       "      <td>-0.000140</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>-0.000144</td>\n",
       "      <td>0.010219</td>\n",
       "      <td>0.008541</td>\n",
       "      <td>-0.003446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>-0.001337</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.025522</td>\n",
       "      <td>0.014032</td>\n",
       "      <td>0.002328</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>-0.000485</td>\n",
       "      <td>-0.000514</td>\n",
       "      <td>-0.000184</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000174</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>-0.000164</td>\n",
       "      <td>-0.000168</td>\n",
       "      <td>0.012705</td>\n",
       "      <td>-0.000173</td>\n",
       "      <td>0.027515</td>\n",
       "      <td>0.023072</td>\n",
       "      <td>-0.003399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>0.002051</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.020168</td>\n",
       "      <td>-0.000583</td>\n",
       "      <td>0.016743</td>\n",
       "      <td>0.010860</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>0.006336</td>\n",
       "      <td>-0.000197</td>\n",
       "      <td>-0.000222</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.484331</td>\n",
       "      <td>0.938668</td>\n",
       "      <td>0.953411</td>\n",
       "      <td>0.021540</td>\n",
       "      <td>-0.000185</td>\n",
       "      <td>0.012393</td>\n",
       "      <td>0.014523</td>\n",
       "      <td>-0.000773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>-0.008500</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.011550</td>\n",
       "      <td>-0.000337</td>\n",
       "      <td>-0.001662</td>\n",
       "      <td>-0.001706</td>\n",
       "      <td>-0.000301</td>\n",
       "      <td>-0.000318</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000128</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>0.484331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.193281</td>\n",
       "      <td>0.225912</td>\n",
       "      <td>-0.000170</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>-0.000162</td>\n",
       "      <td>-0.000174</td>\n",
       "      <td>-0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0.006554</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.019325</td>\n",
       "      <td>-0.000548</td>\n",
       "      <td>0.020509</td>\n",
       "      <td>0.012963</td>\n",
       "      <td>0.002590</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>-0.000185</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000164</td>\n",
       "      <td>0.938668</td>\n",
       "      <td>0.193281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998497</td>\n",
       "      <td>0.032162</td>\n",
       "      <td>-0.000174</td>\n",
       "      <td>0.018565</td>\n",
       "      <td>0.021742</td>\n",
       "      <td>-0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>0.005907</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>0.019527</td>\n",
       "      <td>-0.000561</td>\n",
       "      <td>0.021276</td>\n",
       "      <td>0.013553</td>\n",
       "      <td>0.003867</td>\n",
       "      <td>0.003707</td>\n",
       "      <td>-0.000189</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000168</td>\n",
       "      <td>0.953411</td>\n",
       "      <td>0.225912</td>\n",
       "      <td>0.998497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030087</td>\n",
       "      <td>-0.000178</td>\n",
       "      <td>0.017358</td>\n",
       "      <td>0.020331</td>\n",
       "      <td>-0.000589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0.008825</td>\n",
       "      <td>0.000922</td>\n",
       "      <td>0.041321</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>-0.001905</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>-0.000818</td>\n",
       "      <td>-0.000866</td>\n",
       "      <td>-0.000309</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012705</td>\n",
       "      <td>0.021540</td>\n",
       "      <td>-0.000170</td>\n",
       "      <td>0.032162</td>\n",
       "      <td>0.030087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329805</td>\n",
       "      <td>0.935317</td>\n",
       "      <td>0.919036</td>\n",
       "      <td>0.011106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>-0.009174</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>0.016172</td>\n",
       "      <td>-0.000577</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>0.007096</td>\n",
       "      <td>-0.000515</td>\n",
       "      <td>-0.000545</td>\n",
       "      <td>-0.000195</td>\n",
       "      <td>-0.000220</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000173</td>\n",
       "      <td>-0.000185</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>-0.000174</td>\n",
       "      <td>-0.000178</td>\n",
       "      <td>0.329805</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127224</td>\n",
       "      <td>0.140902</td>\n",
       "      <td>0.011807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.012031</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.043577</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>-0.002552</td>\n",
       "      <td>-0.001672</td>\n",
       "      <td>-0.000779</td>\n",
       "      <td>-0.000825</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027515</td>\n",
       "      <td>0.012393</td>\n",
       "      <td>-0.000162</td>\n",
       "      <td>0.018565</td>\n",
       "      <td>0.017358</td>\n",
       "      <td>0.935317</td>\n",
       "      <td>0.127224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993536</td>\n",
       "      <td>0.008604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.012128</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>0.044281</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>-0.002736</td>\n",
       "      <td>-0.001844</td>\n",
       "      <td>-0.000839</td>\n",
       "      <td>-0.000888</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-0.000358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023072</td>\n",
       "      <td>0.014523</td>\n",
       "      <td>-0.000174</td>\n",
       "      <td>0.021742</td>\n",
       "      <td>0.020331</td>\n",
       "      <td>0.919036</td>\n",
       "      <td>0.140902</td>\n",
       "      <td>0.993536</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0.006612</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>-0.000810</td>\n",
       "      <td>0.000966</td>\n",
       "      <td>0.003656</td>\n",
       "      <td>0.002257</td>\n",
       "      <td>0.004448</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>-0.000739</td>\n",
       "      <td>-0.000811</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003399</td>\n",
       "      <td>-0.000773</td>\n",
       "      <td>-0.000402</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>-0.000589</td>\n",
       "      <td>0.011106</td>\n",
       "      <td>0.011807</td>\n",
       "      <td>0.008604</td>\n",
       "      <td>0.009136</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>227 rows × 227 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6    \\\n",
       "0    1.000000 -0.025277 -0.001942  0.003594  0.004054 -0.001697 -0.015882   \n",
       "1   -0.025277  1.000000 -0.007647  0.001819  0.008981  0.009232  0.001638   \n",
       "2   -0.001942 -0.007647  1.000000  0.030919  0.106245  0.109140  0.048524   \n",
       "3    0.003594  0.001819  0.030919  1.000000  0.029418  0.024905  0.014513   \n",
       "4    0.004054  0.008981  0.106245  0.029418  1.000000  0.888789  0.381632   \n",
       "5   -0.001697  0.009232  0.109140  0.024905  0.888789  1.000000  0.363680   \n",
       "6   -0.015882  0.001638  0.048524  0.014513  0.381632  0.363680  1.000000   \n",
       "7   -0.019807  0.001746  0.055708  0.013857  0.341266  0.384820  0.908158   \n",
       "8    0.000956  0.000614  0.004040 -0.000613  0.012927  0.017671  0.030397   \n",
       "9   -0.000588  0.000695  0.005796 -0.000691  0.019674  0.030060  0.036359   \n",
       "10  -0.012443  0.001517  0.042368  0.012451  0.298916  0.280081  0.805265   \n",
       "11   0.010319  0.009097  0.096719  0.026377  0.938409  0.824893  0.038751   \n",
       "12   0.005268  0.009360  0.098070  0.021968  0.838953  0.943622  0.067664   \n",
       "13   0.017605 -0.002511  0.082025  0.016331  0.266746  0.254702  0.040788   \n",
       "14   0.016960 -0.001086  0.095485  0.016458  0.326051  0.359897  0.048914   \n",
       "15   0.018040  0.002426  0.106415  0.024014  0.638412  0.565620  0.043920   \n",
       "16   0.017400 -0.002401  0.081028  0.015979  0.263482  0.252160  0.043357   \n",
       "17   0.016745 -0.001019  0.095009  0.016239  0.324417  0.358769  0.051373   \n",
       "18   0.015206  0.002629  0.110912  0.025558  0.673593  0.599584  0.190138   \n",
       "19  -0.000103  0.000519  0.016886 -0.000520  0.049579  0.042621  0.012454   \n",
       "20  -0.001198  0.004590  0.107680  0.007478  0.227803  0.238159  0.306165   \n",
       "21  -0.006814 -0.008975 -0.105502 -0.002101 -0.208030 -0.211873 -0.071459   \n",
       "22  -0.002037  0.041015 -0.102487  0.017541  0.041167  0.041372 -0.006549   \n",
       "23   0.010356  0.008019  0.107570  0.003429  0.200514  0.182937  0.035401   \n",
       "24   0.012021  0.007439  0.101605  0.004843  0.220673  0.201909  0.039018   \n",
       "25   0.001732  0.011525  0.273152  0.010099  0.027387  0.026378  0.046258   \n",
       "26   0.001138  0.009467  0.231649  0.015117  0.033757  0.037053  0.044225   \n",
       "27  -0.004836  0.009771  0.299165  0.036569 -0.010411 -0.013701  0.020327   \n",
       "28  -0.006480  0.008796  0.241707  0.040420 -0.012628 -0.018755  0.009992   \n",
       "29  -0.005811  0.008676  0.237830  0.041165 -0.012035 -0.018146  0.010326   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "215  0.006937  0.002152  0.043278  0.002314  0.073627  0.084908  0.009789   \n",
       "216  0.004924  0.002210  0.045622  0.003234  0.086904  0.093401  0.042081   \n",
       "217  0.008100  0.003979  0.149586  0.001554 -0.003401 -0.004867  0.024589   \n",
       "218 -0.000582  0.002581  0.093124 -0.001262 -0.007050 -0.006547 -0.002282   \n",
       "219  0.007130  0.004811  0.178546  0.002540 -0.002079 -0.004782  0.036168   \n",
       "220  0.007675  0.004879  0.179565  0.002948 -0.001151 -0.003808  0.038964   \n",
       "221 -0.006477  0.005759  0.178263 -0.005438  0.002963 -0.001631  0.048470   \n",
       "222 -0.010219  0.003183  0.094741 -0.003083  0.040691  0.027749  0.133603   \n",
       "223 -0.011386  0.006355  0.200415  0.025778 -0.000914 -0.005809  0.038767   \n",
       "224 -0.011200  0.006248  0.195652  0.033042 -0.000322 -0.005729  0.043137   \n",
       "225  0.006455  0.002629  0.125618 -0.001532  0.003267  0.004018  0.013532   \n",
       "226  0.008361  0.001482  0.059293  0.000238  0.012429  0.010896  0.001034   \n",
       "227  0.003765  0.002827  0.135362 -0.001817  0.000824  0.001493  0.012108   \n",
       "228  0.005352  0.002770  0.132537 -0.001698  0.001272  0.001627  0.010082   \n",
       "229  0.008042  0.000356  0.023435 -0.000354 -0.001629 -0.001719 -0.000318   \n",
       "230  0.007870  0.000338  0.022679 -0.000338 -0.001669 -0.001713 -0.000302   \n",
       "231  0.007952  0.000411  0.025362 -0.000285 -0.001677 -0.001846 -0.000365   \n",
       "232  0.008021  0.000408  0.025406 -0.000334 -0.001730 -0.001875 -0.000362   \n",
       "233 -0.001596  0.000391  0.013612 -0.000391 -0.001930 -0.001981 -0.000349   \n",
       "234  0.001830  0.000453  0.023446  0.008469  0.000833 -0.000407 -0.000404   \n",
       "235 -0.001337  0.000544  0.025522  0.014032  0.002328  0.000328 -0.000485   \n",
       "236  0.002051  0.000586  0.020168 -0.000583  0.016743  0.010860  0.006351   \n",
       "237 -0.008500  0.000337  0.011550 -0.000337 -0.001662 -0.001706 -0.000301   \n",
       "238  0.006554  0.000550  0.019325 -0.000548  0.020509  0.012963  0.002590   \n",
       "239  0.005907  0.000563  0.019527 -0.000561  0.021276  0.013553  0.003867   \n",
       "240  0.008825  0.000922  0.041321  0.000541 -0.001905  0.000871 -0.000818   \n",
       "241 -0.009174  0.000598  0.016172 -0.000577 -0.000635  0.007096 -0.000515   \n",
       "242  0.012031  0.000875  0.043577  0.000231 -0.002552 -0.001672 -0.000779   \n",
       "243  0.012128  0.000942  0.044281  0.000235 -0.002736 -0.001844 -0.000839   \n",
       "244  0.006612  0.000415 -0.000810  0.000966  0.003656  0.002257  0.004448   \n",
       "\n",
       "          7         8         9    ...       235       236       237  \\\n",
       "0   -0.019807  0.000956 -0.000588  ... -0.001337  0.002051 -0.008500   \n",
       "1    0.001746  0.000614  0.000695  ...  0.000544  0.000586  0.000337   \n",
       "2    0.055708  0.004040  0.005796  ...  0.025522  0.020168  0.011550   \n",
       "3    0.013857 -0.000613 -0.000691  ...  0.014032 -0.000583 -0.000337   \n",
       "4    0.341266  0.012927  0.019674  ...  0.002328  0.016743 -0.001662   \n",
       "5    0.384820  0.017671  0.030060  ...  0.000328  0.010860 -0.001706   \n",
       "6    0.908158  0.030397  0.036359  ... -0.000485  0.006351 -0.000301   \n",
       "7    1.000000  0.047667  0.056456  ... -0.000514  0.006336 -0.000318   \n",
       "8    0.047667  1.000000  0.988256  ... -0.000184 -0.000197 -0.000114   \n",
       "9    0.056456  0.988256  1.000000  ... -0.000207 -0.000222 -0.000128   \n",
       "10   0.706608  0.388309  0.398826  ... -0.000452 -0.000485 -0.000280   \n",
       "11   0.029445  0.002612  0.007677  ...  0.002699  0.015727 -0.001684   \n",
       "12   0.057591  0.002018  0.012266  ...  0.000540  0.009474 -0.001732   \n",
       "13   0.033996  0.108329  0.106806  ... -0.001670  0.034002 -0.001035   \n",
       "14   0.045136  0.081030  0.081962  ... -0.002040  0.025566 -0.001264   \n",
       "15   0.033716  0.083438  0.084688  ...  0.000009  0.033265 -0.001589   \n",
       "16   0.038548  0.214397  0.211633  ... -0.001661  0.033386 -0.001029   \n",
       "17   0.049260  0.160240  0.162113  ... -0.002037  0.025295 -0.001262   \n",
       "18   0.162168  0.152035  0.155173  ... -0.000074  0.032155 -0.001591   \n",
       "19   0.007797 -0.000175 -0.000198  ... -0.000156 -0.000167 -0.000096   \n",
       "20   0.284353  0.125108  0.138077  ... -0.001365 -0.001462 -0.000846   \n",
       "21  -0.078593 -0.012763 -0.021318  ...  0.002674 -0.049920 -0.037729   \n",
       "22  -0.008179  0.003576  0.001088  ...  0.009114 -0.012641 -0.011070   \n",
       "23   0.025512  0.012907  0.018006  ... -0.002401  0.031418 -0.001487   \n",
       "24   0.028469  0.014240  0.019760  ... -0.002227  0.034079 -0.001380   \n",
       "25   0.033114 -0.003889 -0.004384  ... -0.003450  0.020817 -0.002138   \n",
       "26   0.034049 -0.003194 -0.003600  ... -0.002834  0.026148 -0.001756   \n",
       "27   0.019508 -0.003295 -0.003715  ... -0.002924 -0.003132 -0.001811   \n",
       "28   0.003331 -0.002969 -0.003347  ... -0.002634 -0.002822 -0.001632   \n",
       "29   0.003592 -0.002929 -0.003301  ... -0.002599 -0.002784 -0.001610   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "215  0.009653  0.000540  0.013546  ... -0.000644  0.003153 -0.000399   \n",
       "216  0.029906  0.000420  0.012702  ... -0.000662  0.003721 -0.000410   \n",
       "217  0.017603 -0.001336 -0.001506  ... -0.001185  0.011108 -0.000734   \n",
       "218 -0.002111 -0.000865 -0.000976  ... -0.000768  0.012837 -0.000476   \n",
       "219  0.025934 -0.001618 -0.001823  ... -0.001435  0.010157 -0.000889   \n",
       "220  0.028107 -0.001640 -0.001849  ... -0.001455  0.009732 -0.000902   \n",
       "221  0.029154 -0.001943 -0.002190  ... -0.001724 -0.001846 -0.001068   \n",
       "222  0.084716 -0.001073 -0.001210  ... -0.000952 -0.001020 -0.000590   \n",
       "223  0.022672 -0.002144 -0.002417  ... -0.001903 -0.002038 -0.001179   \n",
       "224  0.025504 -0.002108 -0.002376  ... -0.001871 -0.002004 -0.001159   \n",
       "225  0.021485 -0.000883 -0.000995  ... -0.000783 -0.000839 -0.000485   \n",
       "226  0.002878 -0.000492 -0.000555  ... -0.000437 -0.000468 -0.000271   \n",
       "227  0.019415 -0.000950 -0.001071  ... -0.000843 -0.000903 -0.000522   \n",
       "228  0.016429 -0.000930 -0.001048  ... -0.000825 -0.000884 -0.000511   \n",
       "229 -0.000337 -0.000120 -0.000136  ... -0.000107  0.002856 -0.000066   \n",
       "230 -0.000320 -0.000114 -0.000129  ... -0.000101 -0.000108 -0.000063   \n",
       "231 -0.000387 -0.000138 -0.000156  ... -0.000123  0.007701 -0.000076   \n",
       "232 -0.000383 -0.000137 -0.000154  ... -0.000121  0.006078 -0.000075   \n",
       "233 -0.000370 -0.000132 -0.000149  ...  0.538270 -0.000125 -0.000073   \n",
       "234 -0.000428 -0.000153 -0.000172  ...  0.950232 -0.000145 -0.000084   \n",
       "235 -0.000514 -0.000184 -0.000207  ...  1.000000 -0.000174 -0.000101   \n",
       "236  0.006336 -0.000197 -0.000222  ... -0.000174  1.000000  0.484331   \n",
       "237 -0.000318 -0.000114 -0.000128  ... -0.000101  0.484331  1.000000   \n",
       "238  0.002476 -0.000185 -0.000208  ... -0.000164  0.938668  0.193281   \n",
       "239  0.003707 -0.000189 -0.000213  ... -0.000168  0.953411  0.225912   \n",
       "240 -0.000866 -0.000309 -0.000349  ...  0.012705  0.021540 -0.000170   \n",
       "241 -0.000545 -0.000195 -0.000220  ... -0.000173 -0.000185 -0.000107   \n",
       "242 -0.000825 -0.000295 -0.000332  ...  0.027515  0.012393 -0.000162   \n",
       "243 -0.000888 -0.000317 -0.000358  ...  0.023072  0.014523 -0.000174   \n",
       "244  0.002427 -0.000739 -0.000811  ... -0.003399 -0.000773 -0.000402   \n",
       "\n",
       "          238       239       240       241       242       243       244  \n",
       "0    0.006554  0.005907  0.008825 -0.009174  0.012031  0.012128  0.006612  \n",
       "1    0.000550  0.000563  0.000922  0.000598  0.000875  0.000942  0.000415  \n",
       "2    0.019325  0.019527  0.041321  0.016172  0.043577  0.044281 -0.000810  \n",
       "3   -0.000548 -0.000561  0.000541 -0.000577  0.000231  0.000235  0.000966  \n",
       "4    0.020509  0.021276 -0.001905 -0.000635 -0.002552 -0.002736  0.003656  \n",
       "5    0.012963  0.013553  0.000871  0.007096 -0.001672 -0.001844  0.002257  \n",
       "6    0.002590  0.003867 -0.000818 -0.000515 -0.000779 -0.000839  0.004448  \n",
       "7    0.002476  0.003707 -0.000866 -0.000545 -0.000825 -0.000888  0.002427  \n",
       "8   -0.000185 -0.000189 -0.000309 -0.000195 -0.000295 -0.000317 -0.000739  \n",
       "9   -0.000208 -0.000213 -0.000349 -0.000220 -0.000332 -0.000358 -0.000811  \n",
       "10  -0.000213 -0.000100 -0.000762 -0.000480 -0.000726 -0.000782  0.003341  \n",
       "11   0.021203  0.021555 -0.001754 -0.000494 -0.002467 -0.002645  0.002290  \n",
       "12   0.013133  0.013330  0.001253  0.007871 -0.001513 -0.001676  0.001570  \n",
       "13   0.038103  0.038047  0.007400  0.002248  0.006688  0.006283  0.000707  \n",
       "14   0.028641  0.028613  0.004485  0.001176  0.004012  0.003599 -0.001992  \n",
       "15   0.038978  0.039103  0.004773  0.001465  0.003984  0.003632  0.001339  \n",
       "16   0.037417  0.037362  0.007237  0.002188  0.006539  0.006139  0.000614  \n",
       "17   0.028341  0.028312  0.004412  0.001147  0.003946  0.003534 -0.002038  \n",
       "18   0.037742  0.037884  0.004487  0.001333  0.003729  0.003377  0.001910  \n",
       "19  -0.000156 -0.000160 -0.000262 -0.000165 -0.000250 -0.000269  0.000213  \n",
       "20   0.000768  0.001829  0.009008 -0.001449  0.009156  0.011164 -0.001227  \n",
       "21  -0.038529 -0.041438 -0.000421 -0.010257  0.002149  0.002306 -0.016447  \n",
       "22  -0.008324 -0.009368  0.013263  0.004114  0.013717  0.014768 -0.056029  \n",
       "23   0.033291  0.034573  0.001397  0.011918 -0.001487 -0.001591  0.012346  \n",
       "24   0.036066  0.037450  0.002086  0.013156 -0.001036 -0.001105 -0.003767  \n",
       "25   0.022279  0.023163 -0.000543 -0.003662 -0.001555 -0.001463  0.012034  \n",
       "26   0.027806  0.028888  0.001500 -0.003008  0.000193  0.000460  0.006643  \n",
       "27  -0.001902 -0.001440 -0.003893 -0.003103 -0.003587 -0.003989  0.012240  \n",
       "28  -0.001507 -0.000986 -0.004438 -0.002796 -0.004228 -0.004553  0.007400  \n",
       "29  -0.001456 -0.000927 -0.004378 -0.002758 -0.004171 -0.004491  0.006121  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "215  0.003511  0.003709  0.082936  0.230452  0.029915  0.033617  0.000338  \n",
       "216  0.004129  0.004359  0.074334  0.206808  0.026758  0.030066  0.000244  \n",
       "217  0.011385  0.011627 -0.001886 -0.001258 -0.001828 -0.001966  0.017276  \n",
       "218  0.013076  0.013340 -0.001294 -0.000815 -0.001232 -0.001327  0.006644  \n",
       "219  0.010430  0.010646 -0.002222 -0.001523 -0.002103 -0.002237  0.018092  \n",
       "220  0.010011  0.010224 -0.002257 -0.001545 -0.002116 -0.002243  0.017579  \n",
       "221 -0.001729 -0.001768 -0.002904 -0.001829 -0.002766 -0.002979  0.014736  \n",
       "222 -0.000958 -0.000981 -0.001604 -0.001011 -0.001528 -0.001646  0.002052  \n",
       "223 -0.001788 -0.001769 -0.003205 -0.002019 -0.003054 -0.003288  0.014980  \n",
       "224 -0.001801 -0.001804 -0.003151 -0.001985 -0.003002 -0.003233  0.014628  \n",
       "225 -0.000788 -0.000807 -0.001195 -0.000831 -0.001146 -0.001241  0.014567  \n",
       "226 -0.000439 -0.000450 -0.000573 -0.000463 -0.000556 -0.000608  0.005688  \n",
       "227 -0.000848 -0.000868 -0.001417 -0.000895 -0.001348 -0.001452  0.015351  \n",
       "228 -0.000830 -0.000850 -0.001387 -0.000876 -0.001318 -0.001421  0.014485  \n",
       "229  0.004306  0.004022  0.000265 -0.000113  0.000089  0.000121  0.013197  \n",
       "230 -0.000102 -0.000104 -0.000171 -0.000107 -0.000163 -0.000175  0.012842  \n",
       "231  0.011513  0.010768  0.001004 -0.000130  0.000510  0.000619  0.013321  \n",
       "232  0.009101  0.008510  0.000746 -0.000129  0.000360  0.000443  0.013418  \n",
       "233 -0.000118 -0.000121  0.031970 -0.000124  0.068648  0.057673 -0.000203  \n",
       "234 -0.000136 -0.000140  0.004649 -0.000144  0.010219  0.008541 -0.003446  \n",
       "235 -0.000164 -0.000168  0.012705 -0.000173  0.027515  0.023072 -0.003399  \n",
       "236  0.938668  0.953411  0.021540 -0.000185  0.012393  0.014523 -0.000773  \n",
       "237  0.193281  0.225912 -0.000170 -0.000107 -0.000162 -0.000174 -0.000402  \n",
       "238  1.000000  0.998497  0.032162 -0.000174  0.018565  0.021742 -0.000525  \n",
       "239  0.998497  1.000000  0.030087 -0.000178  0.017358  0.020331 -0.000589  \n",
       "240  0.032162  0.030087  1.000000  0.329805  0.935317  0.919036  0.011106  \n",
       "241 -0.000174 -0.000178  0.329805  1.000000  0.127224  0.140902  0.011807  \n",
       "242  0.018565  0.017358  0.935317  0.127224  1.000000  0.993536  0.008604  \n",
       "243  0.021742  0.020331  0.919036  0.140902  0.993536  1.000000  0.009136  \n",
       "244 -0.000525 -0.000589  0.011106  0.011807  0.008604  0.009136  1.000000  \n",
       "\n",
       "[227 rows x 227 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "     11     0.010319\n",
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       "     14     0.016960\n",
       "     15     0.018040\n",
       "     16     0.017400\n",
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       "              ...   \n",
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       "     236    0.000773\n",
       "     237    0.000402\n",
       "     238    0.000525\n",
       "     239    0.000589\n",
       "     240    0.011106\n",
       "     241    0.011807\n",
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       "Length: 51529, dtype: float64"
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     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdata = corrmat.abs().stack()\n",
    "corrdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29   58     1.000000e+00\n",
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       "182  182    1.000000e+00\n",
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       "179  179    1.000000e+00\n",
       "180  180    1.000000e+00\n",
       "172  172    1.000000e+00\n",
       "                ...     \n",
       "113  60     8.925381e-06\n",
       "60   113    8.925381e-06\n",
       "82   193    8.892757e-06\n",
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       "110  230    8.848510e-06\n",
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       "189  103    5.738723e-06\n",
       "13   120    5.200500e-06\n",
       "120  13     5.200500e-06\n",
       "243  162    3.905074e-06\n",
       "162  243    3.905074e-06\n",
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       "126  186    3.594093e-06\n",
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       "111  229    1.934954e-06\n",
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       "231  150    6.044672e-07\n",
       "150  231    6.044672e-07\n",
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       "Length: 51529, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdata = corrdata.sort_values(ascending=False)\n",
    "corrdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "122  238    0.999945\n",
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       "149  148    0.999929\n",
       "237  148    0.999929\n",
       "148  237    0.999929\n",
       "231  232    0.999892\n",
       "232  231    0.999892\n",
       "              ...   \n",
       "183  52     0.860163\n",
       "52   183    0.860163\n",
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       "23   183    0.860163\n",
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       "195  79     0.859806\n",
       "8    193    0.859270\n",
       "193  8      0.859270\n",
       "29   61     0.858830\n",
       "61   29     0.858830\n",
       "     58     0.858830\n",
       "58   61     0.858830\n",
       "84   77     0.858529\n",
       "77   84     0.858529\n",
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       "173  151    0.854991\n",
       "41   163    0.852233\n",
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       "Length: 534, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdata = corrdata[corrdata>0.85]\n",
    "corrdata = corrdata[corrdata<1]\n",
    "corrdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
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       "      <th>504</th>\n",
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       "      <th>507</th>\n",
       "      <td>23</td>\n",
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       "      <th>510</th>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>511</th>\n",
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       "      <td>8</td>\n",
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       "      <th>512</th>\n",
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       "      <td>61</td>\n",
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       "      <th>513</th>\n",
       "      <td>61</td>\n",
       "      <td>29</td>\n",
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       "      <td>61</td>\n",
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       "      <th>516</th>\n",
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       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>77</td>\n",
       "      <td>84</td>\n",
       "      <td>0.858529</td>\n",
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       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>83</td>\n",
       "      <td>189</td>\n",
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       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>189</td>\n",
       "      <td>83</td>\n",
       "      <td>0.858484</td>\n",
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       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>84</td>\n",
       "      <td>194</td>\n",
       "      <td>0.857731</td>\n",
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       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>194</td>\n",
       "      <td>84</td>\n",
       "      <td>0.857731</td>\n",
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       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>76</td>\n",
       "      <td>190</td>\n",
       "      <td>0.857717</td>\n",
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       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>190</td>\n",
       "      <td>76</td>\n",
       "      <td>0.857717</td>\n",
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       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>151</td>\n",
       "      <td>173</td>\n",
       "      <td>0.854991</td>\n",
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       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>173</td>\n",
       "      <td>151</td>\n",
       "      <td>0.854991</td>\n",
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       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>41</td>\n",
       "      <td>163</td>\n",
       "      <td>0.852233</td>\n",
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       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>163</td>\n",
       "      <td>41</td>\n",
       "      <td>0.852233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>0.851384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>67</td>\n",
       "      <td>66</td>\n",
       "      <td>0.851384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>61</td>\n",
       "      <td>28</td>\n",
       "      <td>0.851022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>28</td>\n",
       "      <td>61</td>\n",
       "      <td>0.851022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0.850893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>0.850893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>534 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     features1  features2  corr_value\n",
       "0          143        135    1.000000\n",
       "1          135        143    1.000000\n",
       "2          136        128    1.000000\n",
       "3          128        136    1.000000\n",
       "4           31         62    1.000000\n",
       "5           62         31    1.000000\n",
       "6           20         47    1.000000\n",
       "7           47         20    1.000000\n",
       "8           52         23    1.000000\n",
       "9           23         52    1.000000\n",
       "10          53         24    1.000000\n",
       "11          24         53    1.000000\n",
       "12          33         69    1.000000\n",
       "13          69         33    1.000000\n",
       "14         157        133    1.000000\n",
       "15         133        157    1.000000\n",
       "16         237        149    1.000000\n",
       "17         149        237    1.000000\n",
       "18         154        132    1.000000\n",
       "19         132        154    1.000000\n",
       "20         146        230    0.999997\n",
       "21         230        146    0.999997\n",
       "22         238        122    0.999945\n",
       "23         122        238    0.999945\n",
       "24         148        149    0.999929\n",
       "25         149        148    0.999929\n",
       "26         237        148    0.999929\n",
       "27         148        237    0.999929\n",
       "28         231        232    0.999892\n",
       "29         232        231    0.999892\n",
       "..         ...        ...         ...\n",
       "504        183         52    0.860163\n",
       "505         52        183    0.860163\n",
       "506        183         23    0.860163\n",
       "507         23        183    0.860163\n",
       "508         79        195    0.859806\n",
       "509        195         79    0.859806\n",
       "510          8        193    0.859270\n",
       "511        193          8    0.859270\n",
       "512         29         61    0.858830\n",
       "513         61         29    0.858830\n",
       "514         61         58    0.858830\n",
       "515         58         61    0.858830\n",
       "516         84         77    0.858529\n",
       "517         77         84    0.858529\n",
       "518         83        189    0.858484\n",
       "519        189         83    0.858484\n",
       "520         84        194    0.857731\n",
       "521        194         84    0.857731\n",
       "522         76        190    0.857717\n",
       "523        190         76    0.857717\n",
       "524        151        173    0.854991\n",
       "525        173        151    0.854991\n",
       "526         41        163    0.852233\n",
       "527        163         41    0.852233\n",
       "528         66         67    0.851384\n",
       "529         67         66    0.851384\n",
       "530         61         28    0.851022\n",
       "531         28         61    0.851022\n",
       "532         72         35    0.850893\n",
       "533         35         72    0.850893\n",
       "\n",
       "[534 rows x 3 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdata = pd.DataFrame(corrdata).reset_index()\n",
    "corrdata.columns = ['features1', 'features2', 'corr_value']\n",
    "corrdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_feature_list = []\n",
    "correlated_groups_list = []\n",
    "for feature in corrdata.features1.unique():\n",
    "    if feature not in grouped_feature_list:\n",
    "        correlated_block = corrdata[corrdata.features1 == feature]\n",
    "        grouped_feature_list = grouped_feature_list + list(correlated_block.features2.unique()) + [feature]\n",
    "        correlated_groups_list.append(correlated_block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(correlated_groups_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16000, 370), (16000, 103))"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_train_uncorr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   features1  features2  corr_value\n",
      "0        143        135         1.0\n",
      "     features1  features2  corr_value\n",
      "2          136        128    1.000000\n",
      "197        136        169    0.959468\n",
      "   features1  features2  corr_value\n",
      "4         31         62         1.0\n",
      "   features1  features2  corr_value\n",
      "6         20         47         1.0\n",
      "     features1  features2  corr_value\n",
      "8           52         23    1.000000\n",
      "297         52         24    0.927683\n",
      "299         52         53    0.927683\n",
      "448         52         21    0.877297\n",
      "505         52        183    0.860163\n",
      "     features1  features2  corr_value\n",
      "12          33         69    1.000000\n",
      "224         33         32    0.947113\n",
      "228         33         68    0.946571\n",
      "322         33         26    0.917665\n",
      "337         33         55    0.914178\n",
      "422         33        184    0.884383\n",
      "    features1  features2  corr_value\n",
      "14        157        133         1.0\n",
      "    features1  features2  corr_value\n",
      "16        237        149    1.000000\n",
      "26        237        148    0.999929\n",
      "    features1  features2  corr_value\n",
      "18        154        132         1.0\n",
      "     features1  features2  corr_value\n",
      "20         146        230    0.999997\n",
      "36         146        229    0.999778\n",
      "59         146        231    0.997052\n",
      "68         146        232    0.996772\n",
      "76         146        113    0.996424\n",
      "89         146        120    0.993307\n",
      "245        146        170    0.944314\n",
      "     features1  features2  corr_value\n",
      "22         238        122    0.999945\n",
      "49         238        239    0.998497\n",
      "264        238        236    0.938668\n",
      "    features1  features2  corr_value\n",
      "34         82         78    0.999859\n",
      "     features1  features2  corr_value\n",
      "40         108        115    0.999478\n",
      "97         108        219    0.992870\n",
      "115        108        125    0.987333\n",
      "142        108        220    0.982474\n",
      "280        108        217    0.933815\n",
      "     features1  features2  corr_value\n",
      "46         199        197    0.998753\n",
      "362        199        196    0.905699\n",
      "371        199        198    0.904341\n",
      "     features1  features2  corr_value\n",
      "50         181        208    0.997718\n",
      "345        181        205    0.911453\n",
      "467        181        207    0.871801\n",
      "     features1  features2  corr_value\n",
      "72          17         14    0.996739\n",
      "396         17         16    0.890442\n",
      "408         17         13    0.888669\n",
      "     features1  features2  corr_value\n",
      "86         242        243    0.993536\n",
      "122        242        126    0.986744\n",
      "276        242        240    0.935317\n",
      "     features1  features2  corr_value\n",
      "92          28         57    0.993186\n",
      "124         28         58    0.986371\n",
      "126         28         29    0.986371\n",
      "185         28        185    0.964067\n",
      "381         28         27    0.901032\n",
      "399         28         30    0.889321\n",
      "531         28         61    0.851022\n",
      "     features1  features2  corr_value\n",
      "94          51         22    0.992882\n",
      "385         51        182    0.899063\n",
      "     features1  features2  corr_value\n",
      "100         44         46    0.990593\n",
      "377         44         98    0.902736\n",
      "410         44         95    0.888337\n",
      "     features1  features2  corr_value\n",
      "102         77         81    0.989793\n",
      "461         77         80    0.874240\n",
      "517         77         84    0.858529\n",
      "     features1  features2  corr_value\n",
      "104        109        223    0.989341\n",
      "151        109        224    0.980951\n",
      "356        109        221    0.907987\n",
      "413        109        111    0.887721\n",
      "     features1  features2  corr_value\n",
      "112          9          8    0.988256\n",
      "417          9        193    0.886955\n",
      "444          9        192    0.878045\n",
      "     features1  features2  corr_value\n",
      "116        227        228    0.987304\n",
      "188        227        225    0.962657\n",
      "     features1  features2  corr_value\n",
      "118        116        117    0.987013\n",
      "     features1  features2  corr_value\n",
      "128         91         49    0.985951\n",
      "     features1  features2  corr_value\n",
      "130         54         25    0.985875\n",
      "419         54        100    0.886309\n",
      "     features1  features2  corr_value\n",
      "134         76         75    0.984751\n",
      "353         76         74    0.908497\n",
      "477         76        191    0.870551\n",
      "522         76        190    0.857717\n",
      "     features1  features2  corr_value\n",
      "136         38         35    0.984077\n",
      "261         38         34    0.940390\n",
      "306         38         36    0.922699\n",
      "496         38         72    0.864661\n",
      "     features1  features2  corr_value\n",
      "138         18         15    0.983164\n",
      "465         18         16    0.872133\n",
      "470         18         13    0.870936\n",
      "     features1  features2  corr_value\n",
      "140        215        107    0.983156\n",
      "146        215        216    0.981815\n",
      "     features1  features2  corr_value\n",
      "161         56         61    0.976942\n",
      "187         56         27    0.962726\n",
      "211         56         30    0.953194\n",
      "     features1  features2  corr_value\n",
      "164        162        163    0.975002\n",
      "288        162        161    0.930635\n",
      "369        162        164    0.904702\n",
      "463        162         41    0.874083\n",
      "     features1  features2  corr_value\n",
      "166        102        103    0.974341\n",
      "     features1  features2  corr_value\n",
      "168         83         79    0.973140\n",
      "263         83        188    0.938960\n",
      "273         83         84    0.936080\n",
      "315         83        194    0.919405\n",
      "351         83         80    0.910385\n",
      "518         83        189    0.858484\n",
      "     features1  features2  corr_value\n",
      "174         70         72    0.972088\n",
      "500         70         35    0.862850\n",
      "     features1  features2  corr_value\n",
      "180         59         60    0.968504\n",
      "     features1  features2  corr_value\n",
      "207        195        189    0.956666\n",
      "313        195         80    0.920961\n",
      "330        195        194    0.916442\n",
      "378        195         84    0.902276\n",
      "428        195        188    0.882312\n",
      "509        195         79    0.859806\n",
      "     features1  features2  corr_value\n",
      "216        235        234    0.950232\n",
      "349        235        106    0.911179\n",
      "     features1  features2  corr_value\n",
      "220         10        104    0.948845\n",
      "     features1  features2  corr_value\n",
      "234        180        179    0.945288\n",
      "     features1  features2  corr_value\n",
      "236        241        151    0.944812\n",
      "     features1  features2  corr_value\n",
      "243         42         41    0.944451\n",
      "415         42        161    0.887059\n",
      "503         42        164    0.861507\n",
      "     features1  features2  corr_value\n",
      "248         12          5    0.943622\n",
      "434         12         11    0.881673\n",
      "     features1  features2  corr_value\n",
      "266          4         11    0.938409\n",
      "402          4          5    0.888789\n",
      "     features1  features2  corr_value\n",
      "274         93         92    0.935867\n",
      "     features1  features2  corr_value\n",
      "290         89        121    0.928898\n",
      "     features1  features2  corr_value\n",
      "304         88         87       0.924\n",
      "     features1  features2  corr_value\n",
      "318        174        204    0.918533\n",
      "     features1  features2  corr_value\n",
      "333         50         21    0.916137\n",
      "     features1  features2  corr_value\n",
      "354          6          7    0.908158\n",
      "     features1  features2  corr_value\n",
      "372         64         65    0.904095\n",
      "488         64         87    0.866430\n",
      "     features1  features2  corr_value\n",
      "374        101         86    0.903641\n",
      "394        101         40    0.892951\n",
      "     features1  features2  corr_value\n",
      "390        131        153     0.89633\n",
      "     features1  features2  corr_value\n",
      "525        173        151    0.854991\n",
      "     features1  features2  corr_value\n",
      "528         66         67    0.851384\n"
     ]
    }
   ],
   "source": [
    "for group in correlated_groups_list:\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance based on tree based classifiers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "important_features = []\n",
    "for group in correlated_groups_list:\n",
    "    features = list(group.features1.unique()) + list(group.features2.unique())\n",
    "    rf = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "    rf.fit(X_train_unique[features], y_train)\n",
    "    \n",
    "    importance = pd.concat([pd.Series(features), pd.Series(rf.feature_importances_)], axis = 1)\n",
    "    importance.columns = ['features', 'importance']\n",
    "    importance.sort_values(by = 'importance', ascending = False, inplace = True)\n",
    "    feat = importance.iloc[0]\n",
    "    important_features.append(feat)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[features      135.00\n",
       " importance      0.51\n",
       " Name: 1, dtype: float64, features      128.000000\n",
       " importance      0.563757\n",
       " Name: 1, dtype: float64, features      62.00\n",
       " importance     0.51\n",
       " Name: 1, dtype: float64, features      47.00\n",
       " importance     0.51\n",
       " Name: 1, dtype: float64, features      183.000000\n",
       " importance      0.285817\n",
       " Name: 5, dtype: float64, features      184.00000\n",
       " importance      0.34728\n",
       " Name: 6, dtype: float64, features      157.00\n",
       " importance      0.34\n",
       " Name: 0, dtype: float64, features      148.000000\n",
       " importance      0.505844\n",
       " Name: 2, dtype: float64, features      132.00\n",
       " importance      0.39\n",
       " Name: 1, dtype: float64, features      120.000000\n",
       " importance      0.749683\n",
       " Name: 6, dtype: float64, features      122.00\n",
       " importance      0.34\n",
       " Name: 1, dtype: float64, features      82.000000\n",
       " importance     0.518827\n",
       " Name: 0, dtype: float64, features      125.000000\n",
       " importance      0.940524\n",
       " Name: 3, dtype: float64, features      197.000000\n",
       " importance      0.289727\n",
       " Name: 1, dtype: float64, features      207.000000\n",
       " importance      0.312834\n",
       " Name: 3, dtype: float64, features      17.000000\n",
       " importance     0.286833\n",
       " Name: 0, dtype: float64, features      243.000000\n",
       " importance      0.431557\n",
       " Name: 1, dtype: float64, features      185.000000\n",
       " importance      0.391367\n",
       " Name: 4, dtype: float64, features      182.000000\n",
       " importance      0.432045\n",
       " Name: 2, dtype: float64, features      95.000000\n",
       " importance     0.487162\n",
       " Name: 3, dtype: float64, features      84.000000\n",
       " importance     0.299008\n",
       " Name: 3, dtype: float64, features      221.00000\n",
       " importance      0.28555\n",
       " Name: 3, dtype: float64, features      8.000000\n",
       " importance    0.345509\n",
       " Name: 1, dtype: float64, features      228.000000\n",
       " importance      0.434186\n",
       " Name: 1, dtype: float64, features      117.000000\n",
       " importance      0.517013\n",
       " Name: 1, dtype: float64, features      49.000000\n",
       " importance     0.500161\n",
       " Name: 1, dtype: float64, features      100.000000\n",
       " importance      0.386775\n",
       " Name: 2, dtype: float64, features      191.000000\n",
       " importance      0.345104\n",
       " Name: 3, dtype: float64, features      34.000000\n",
       " importance     0.283901\n",
       " Name: 2, dtype: float64, features      15.000000\n",
       " importance     0.400677\n",
       " Name: 1, dtype: float64, features      107.000000\n",
       " importance      0.349126\n",
       " Name: 1, dtype: float64, features      61.000000\n",
       " importance     0.323735\n",
       " Name: 1, dtype: float64, features      41.000000\n",
       " importance     0.386338\n",
       " Name: 4, dtype: float64, features      102.000000\n",
       " importance      0.508955\n",
       " Name: 0, dtype: float64, features      189.000000\n",
       " importance      0.229269\n",
       " Name: 6, dtype: float64, features      72.000000\n",
       " importance     0.490102\n",
       " Name: 1, dtype: float64, features      60.00000\n",
       " importance     0.50052\n",
       " Name: 1, dtype: float64, features      79.000000\n",
       " importance     0.213903\n",
       " Name: 6, dtype: float64, features      234.000000\n",
       " importance      0.445719\n",
       " Name: 1, dtype: float64, features      104.000000\n",
       " importance      0.640915\n",
       " Name: 1, dtype: float64, features      179.000000\n",
       " importance      0.634779\n",
       " Name: 1, dtype: float64, features      151.00\n",
       " importance      0.51\n",
       " Name: 1, dtype: float64, features      161.000000\n",
       " importance      0.346426\n",
       " Name: 2, dtype: float64, features      5.000000\n",
       " importance    0.356386\n",
       " Name: 1, dtype: float64, features      5.000000\n",
       " importance    0.403831\n",
       " Name: 2, dtype: float64, features      93.000000\n",
       " importance     0.544349\n",
       " Name: 0, dtype: float64, features      121.00\n",
       " importance      0.51\n",
       " Name: 1, dtype: float64, features      87.000000\n",
       " importance     0.553622\n",
       " Name: 1, dtype: float64, features      174.000000\n",
       " importance      0.743723\n",
       " Name: 0, dtype: float64, features      50.000000\n",
       " importance     0.616659\n",
       " Name: 0, dtype: float64, features      7.000000\n",
       " importance    0.545702\n",
       " Name: 1, dtype: float64, features      87.0000\n",
       " importance     0.7462\n",
       " Name: 2, dtype: float64, features      86.000000\n",
       " importance     0.447693\n",
       " Name: 1, dtype: float64, features      153.00\n",
       " importance      0.51\n",
       " Name: 1, dtype: float64, features      151.00\n",
       " importance      0.51\n",
       " Name: 1, dtype: float64, features      66.000000\n",
       " importance     0.630293\n",
       " Name: 0, dtype: float64]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "important_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "important_features = pd.DataFrame(important_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "important_features.reset_index(inplace=True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135.0</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128.0</td>\n",
       "      <td>0.563757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.0</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>183.0</td>\n",
       "      <td>0.285817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>184.0</td>\n",
       "      <td>0.347280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>157.0</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>148.0</td>\n",
       "      <td>0.505844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>132.0</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120.0</td>\n",
       "      <td>0.749683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>122.0</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>82.0</td>\n",
       "      <td>0.518827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>125.0</td>\n",
       "      <td>0.940524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>197.0</td>\n",
       "      <td>0.289727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>207.0</td>\n",
       "      <td>0.312834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.286833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>243.0</td>\n",
       "      <td>0.431557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>185.0</td>\n",
       "      <td>0.391367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>182.0</td>\n",
       "      <td>0.432045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>95.0</td>\n",
       "      <td>0.487162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.299008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>221.0</td>\n",
       "      <td>0.285550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.345509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>228.0</td>\n",
       "      <td>0.434186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>117.0</td>\n",
       "      <td>0.517013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0.500161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.386775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>191.0</td>\n",
       "      <td>0.345104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.283901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.400677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>107.0</td>\n",
       "      <td>0.349126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.323735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.386338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>102.0</td>\n",
       "      <td>0.508955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>189.0</td>\n",
       "      <td>0.229269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.490102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>60.0</td>\n",
       "      <td>0.500520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>79.0</td>\n",
       "      <td>0.213903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>234.0</td>\n",
       "      <td>0.445719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>104.0</td>\n",
       "      <td>0.640915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>179.0</td>\n",
       "      <td>0.634779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>151.0</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>161.0</td>\n",
       "      <td>0.346426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.356386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.403831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>93.0</td>\n",
       "      <td>0.544349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>121.0</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>87.0</td>\n",
       "      <td>0.553622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>174.0</td>\n",
       "      <td>0.743723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.616659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.545702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>87.0</td>\n",
       "      <td>0.746200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>86.0</td>\n",
       "      <td>0.447693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>153.0</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>151.0</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>66.0</td>\n",
       "      <td>0.630293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    features  importance\n",
       "0      135.0    0.510000\n",
       "1      128.0    0.563757\n",
       "2       62.0    0.510000\n",
       "3       47.0    0.510000\n",
       "4      183.0    0.285817\n",
       "5      184.0    0.347280\n",
       "6      157.0    0.340000\n",
       "7      148.0    0.505844\n",
       "8      132.0    0.390000\n",
       "9      120.0    0.749683\n",
       "10     122.0    0.340000\n",
       "11      82.0    0.518827\n",
       "12     125.0    0.940524\n",
       "13     197.0    0.289727\n",
       "14     207.0    0.312834\n",
       "15      17.0    0.286833\n",
       "16     243.0    0.431557\n",
       "17     185.0    0.391367\n",
       "18     182.0    0.432045\n",
       "19      95.0    0.487162\n",
       "20      84.0    0.299008\n",
       "21     221.0    0.285550\n",
       "22       8.0    0.345509\n",
       "23     228.0    0.434186\n",
       "24     117.0    0.517013\n",
       "25      49.0    0.500161\n",
       "26     100.0    0.386775\n",
       "27     191.0    0.345104\n",
       "28      34.0    0.283901\n",
       "29      15.0    0.400677\n",
       "30     107.0    0.349126\n",
       "31      61.0    0.323735\n",
       "32      41.0    0.386338\n",
       "33     102.0    0.508955\n",
       "34     189.0    0.229269\n",
       "35      72.0    0.490102\n",
       "36      60.0    0.500520\n",
       "37      79.0    0.213903\n",
       "38     234.0    0.445719\n",
       "39     104.0    0.640915\n",
       "40     179.0    0.634779\n",
       "41     151.0    0.510000\n",
       "42     161.0    0.346426\n",
       "43       5.0    0.356386\n",
       "44       5.0    0.403831\n",
       "45      93.0    0.544349\n",
       "46     121.0    0.510000\n",
       "47      87.0    0.553622\n",
       "48     174.0    0.743723\n",
       "49      50.0    0.616659\n",
       "50       7.0    0.545702\n",
       "51      87.0    0.746200\n",
       "52      86.0    0.447693\n",
       "53     153.0    0.510000\n",
       "54     151.0    0.510000\n",
       "55      66.0    0.630293"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "important_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_consider = set(important_features['features'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_discard = set(corr_features) - set(features_to_consider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_discard = list(features_to_discard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 140)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_grouped_uncorr = X_train_unique.drop(labels = features_to_discard, axis = 1)\n",
    "X_train_grouped_uncorr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 140)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_grouped_uncorr = X_test_unique.drop(labels=features_to_discard, axis = 1)\n",
    "X_test_grouped_uncorr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.95775\n",
      "Wall time: 1.01 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train_grouped_uncorr, X_test_grouped_uncorr, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.9585\n",
      "Wall time: 1.48 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.95875\n",
      "Wall time: 891 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train_uncorr, X_test_uncorr, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
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